awesome-domain-adaptation
Domain adaptation repository
A collection of resources and research papers on techniques for adapting machine learning models to new domains or tasks with limited data
A collection of AWESOME things about domian adaptation
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adversarial-learningawesome-listdomain-adaptationfew-shot-learningimage-translationoptimal-transportpapertransfer-learningzero-shot-learning
Papers / Survey | |||
| [17 Nov 2022] | Video Unsupervised Domain Adaptation with Deep Learning: A Comprehensive Survey | ||
| [7 Jun 2021] | A Survey on Deep Domain Adaptation for LiDAR Perception | ||
| [7 Nov 2019] | A Comprehensive Survey on Transfer Learning | ||
| [12 Mar 2019] | Transfer Adaptation Learning: A Decade Survey | ||
| [16 Jan 2019] | A review of single-source unsupervised domain adaptation | ||
| [31 Dec 2018] | An introduction to domain adaptation and transfer learning | ||
| [6 Dec 2018] | A Survey of Unsupervised Deep Domain Adaptation | ||
| [2017] | Transfer Learning for Cross-Dataset Recognition: A Survey | ||
| [2017] | Domain Adaptation for Visual Applications: A Comprehensive Survey | ||
| [IEEE Access 2023] | Survey on Unsupervised Domain Adaptation for Semantic Segmentation for Visual Perception in Automated Driving | ||
| [TNNLS 2020] | A Review of Single-Source Deep Unsupervised Visual Domain Adaptation | ||
| [Neurocomputing 2018] | Deep Visual Domain Adaptation: A Survey | ||
| [ICANN2018] | A Survey on Deep Transfer Learning | ||
| [2015] | Visual domain adaptation: A survey of recent advances | ||
Papers / Theory | |||
| [22 Feb 2021] | A Theory of Label Propagation for Subpopulation Shift | ||
| [3 Oct 2019] | A General Upper Bound for Unsupervised Domain Adaptation | ||
| [arXiv 15 Nov 2018] | On Deep Domain Adaptation: Some Theoretical Understandings | ||
| [NeurIPS 2020] | Domain Adaptation with Conditional Distribution Matching and Generalized Label Shift | ||
| [ICML2019] | Bridging Theory and Algorithm for Domain Adaptation | ||
| [ICML2019] | On Learning Invariant Representation for Domain Adaptation | ||
| [AAAI2019] | Unsupervised Domain Adaptation Based on Source-guided Discrepancy | ||
| [NIPS2007] | Learning Bounds for Domain Adaptation | ||
| [NIPS2006] | Analysis of Representations for Domain Adaptation | ||
| [ACHA2021] | On a Regularization of Unsupervised Domain Adaptation in RKHS | ||
| [TPAMI2020] | Unsupervised Multi-Class Domain Adaptation: Theory, Algorithms, and Practice | ||
| [AMAI2020] | On generalization in moment-based domain adaptation | ||
| [ML2010] | A theory of learning from different domains | ||
Papers / Explainable | |||
| [CVPR2021] | Visualizing Adapted Knowledge in Domain Transfer | ||
Papers / Unsupervised DA / Adversarial Methods | |||
| [NeurIPS 2023] | SPA: A Graph Spectral Alignment Perspective for Domain Adaptation | ||
| [CVPR2022] | Reusing the Task-specific Classifier as a Discriminator: Discriminator-free Adversarial Domain Adaptation | ||
| [ICML2022] | A Closer Look at Smoothness in Domain Adversarial Training | ||
| [NeurIPS2021] | ToAlign: Task-oriented Alignment for Unsupervised Domain Adaptation | ||
| [ICCV2021] | Adversarial Unsupervised Domain Adaptation With Conditional and Label Shift: Infer, Align and Iterate | ||
| [ICCV2021] | Gradient Distribution Alignment Certificates Better Adversarial Domain Adaptation | ||
| [ICCV2021] | Re-energizing Domain Discriminator with Sample Relabeling for Adversarial Domain Adaptation | ||
| [CVPR2021] | Cross-Domain Gradient Discrepancy Minimization for Unsupervised Domain Adaptation | ||
| [CVPR2021] | MetaAlign: Coordinating Domain Alignment and Classification for Unsupervised Domain Adaptation | ||
| [IJCAI2020] | Self-adaptive Re-weighted Adversarial Domain Adaptation | ||
| [CoRL2020] | DIRL: Domain-Invariant Reperesentation Learning Approach for Sim-to-Real Transfer | ||
| [ECCV Workshop 2020] | SSA-DA: Bi-dimensional feature alignment for cross-domain object detection | ||
| [ECCV2020] | Classes Matter: A Fine-grained Adversarial Approach to Cross-domain Semantic Segmentation | ||
| [ECCV2020] | MCAR: Adaptive object detection with dual multi-label prediction | ||
| [CVPR2020] | Gradually Vanishing Bridge for Adversarial Domain Adaptation | ||
| [ICML2020] | Implicit Class-Conditioned Domain Alignment for Unsupervised Domain Adaptation | ||
| [AAAI2020] | Adversarial-Learned Loss for Domain Adaptation | ||
| [AAAI2020] | Structure-Aware Feature Fusion for Unsupervised Domain Adaptation | ||
| [AAAI2020] | Adversarial Domain Adaptation with Domain Mixup | ||
| [AAAI2020] | Discriminative Adversarial Domain Adaptation | ||
| [AAAI2020] | Bi-Directional Generation for Unsupervised Domain Adaptation | ||
| [ICASSP 2020] | Cross-stained Segmentation from Renal Biopsy Images Using Multi-level Adversarial Learning | ||
| [BMVC2019] | Curriculum based Dropout Discriminator for Domain Adaptation | ||
| [IJCNN2019] | Unifying Unsupervised Domain Adaptation and Zero-Shot Visual Recognition | ||
| [ICDM2019] | Transfer Learning with Dynamic Adversarial Adaptation Network | ||
| [ACM MM2019] | Joint Adversarial Domain Adaptation | ||
| [ACM MM2019] | Cycle-consistent Conditional Adversarial Transfer Networks | ||
| [IJCAI2019] | Learning Disentangled Semantic Representation for Domain Adaptation | ||
| [ICML2019] | Transferability vs. Discriminability: Batch Spectral Penalization for Adversarial Domain Adaptation | ||
| [ICML2019] | Transferable Adversarial Training: A General Approach to Adapting Deep Classifiers | ||
| [ICCV2019] | Drop to Adapt: Learning Discriminative Features for Unsupervised Domain Adaptation | ||
| [ICCV2019] | Cluster Alignment with a Teacher for Unsupervised Domain Adaptation | ||
| [ICCV2019] | Unsupervised Domain Adaptation via Regularized Conditional Alignment | ||
| [CVPR2019] | Attending to Discriminative Certainty for Domain Adaptation | ||
| [CVPR2019] | GCAN: Graph Convolutional Adversarial Network for Unsupervised Domain Adaptation | ||
| [CVPR2019] | Domain-Symmetric Networks for Adversarial Domain Adaptation | ||
| [CVPR2019 Oral] | DLOW: Domain Flow for Adaptation and Generalization | ||
| [CVPR2019] | Progressive Feature Alignment for Unsupervised Domain Adaptation | ||
| [CVPR2019] | Gotta Adapt ’Em All: Joint Pixel and Feature-Level Domain Adaptation for Recognition in the Wild | ||
| [IJCNN2019] | Looking back at Labels: A Class based Domain Adaptation Technique | ||
| [AAAI2019] | Consensus Adversarial Domain Adaptation | ||
| [AAAI2019] | Transferable Attention for Domain Adaptation | ||
| [AAAI2019] | Exploiting Local Feature Patterns for Unsupervised Domain Adaptation | ||
| [ICLR2019] | Augmented Cyclic Adversarial Learning for Low Resource Domain Adaptation | ||
| [NIPS2018] | Conditional Adversarial Domain Adaptation | ||
| [ECCV2018] | Semi-supervised Adversarial Learning to Generate Photorealistic Face Images of New Identities from 3D Morphable Model | ||
| [ECCV2018] | Deep Adversarial Attention Alignment for Unsupervised Domain Adaptation: the Benefit of Target Expectation Maximization | ||
| [ICML2018] | Learning Semantic Representations for Unsupervised Domain Adaptation | ||
| [ICML2018] | CyCADA: Cycle-Consistent Adversarial Domain Adaptation | ||
| [CVPR2018] | From source to target and back: Symmetric Bi-Directional Adaptive GAN | ||
| [CVPR2018] | Detach and Adapt: Learning Cross-Domain Disentangled Deep Representation | ||
| [CVPR2018] | Maximum Classifier Discrepancy for Unsupervised Domain Adaptation | ||
| [CVPR2018] | Adversarial Feature Augmentation for Unsupervised Domain Adaptation | ||
| [CVPR2018] | Duplex Generative Adversarial Network for Unsupervised Domain Adaptation | ||
| [CVPR2018] | Generate To Adapt: Aligning Domains using Generative Adversarial Networks | ||
| [CVPR2018] | Image to Image Translation for Domain Adaptation | ||
| [CVPR2018] | Unsupervised Domain Adaptation with Similarity Learning | ||
| [CVPR2018] | Conditional Generative Adversarial Network for Structured Domain Adaptation | ||
| [CVPR2018] | Collaborative and Adversarial Network for Unsupervised Domain Adaptation | ||
| [CVPR2018] | Re-Weighted Adversarial Adaptation Network for Unsupervised Domain Adaptation | ||
| [AAAI2018] | Multi-Adversarial Domain Adaptation | ||
| [AAAI2018] | Wasserstein Distance Guided Representation Learning for Domain Adaptation | ||
| [ICRA2018] | Incremental Adversarial Domain Adaptation for Continually Changing Environments | ||
| [ICLR2018] | Adversarial Dropout Regularization | ||
| [ICLR2018 Poster] | A DIRT-T Approach to Unsupervised Domain Adaptation | ||
| [NIPS2017] | Label Efficient Learning of Transferable Representations acrosss Domains and Tasks | ||
| [CVPR2017] | Adversarial Discriminative Domain Adaptation | ||
| [CVPR2017] | Unsupervised Pixel–Level Domain Adaptation with Generative Adversarial Networks | ||
| [NIPS2016] | Domain Separation Networks | ||
| [ECCV2016] | Deep Reconstruction-Classification Networks for Unsupervised Domain Adaptation | ||
| [JMLR2016] | Domain-Adversarial Training of Neural Networks | ||
| [ICML2015] | Unsupervised Domain Adaptation by Backpropagation | ||
| [TNNLS 2020] | Incremental Unsupervised Domain-Adversarial Training of Neural Networks | ||
| [TPAMI2020] | Unsupervised Multi-Class Domain Adaptation: Theory, Algorithms, and Practice | ||
| [IEEE ACCESS] | Adversarial Learning and Interpolation Consistency for Unsupervised Domain Adaptation | ||
| [Knowledge-Based Systems] | TarGAN: Generating target data with class labels for unsupervised domain adaptation | ||
| [12 Feb 2020] | Bi-Directional Generation for Unsupervised Domain Adaptation | ||
| [19 Feb 2020] | Enlarging Discriminative Power by Adding an Extra Class in Unsupervised Domain Adaptation | ||
| [10 Dec 2019] | Learning Domain Adaptive Features with Unlabeled Domain Bridges | ||
| [25 Oct 2019] | Reducing Domain Gap via Style-Agnostic Networks | ||
| [23 Oct 2019] | Generalized Domain Adaptation with Covariate and Label Shift CO-ALignment | ||
| [25 Sep 2019] | Adversarial Variational Domain Adaptation | ||
| [arXiv 13 Sep 2019] | Contrastively Smoothed Class Alignment for Unsupervised Domain Adaptation | ||
| [arXiv 11 Jun 2019] | SALT: Subspace Alignment as an Auxiliary Learning Task for Domain Adaptation | ||
| [arXiv 10 Jun 2019] | Joint Semantic Domain Alignment and Target Classifier Learning for Unsupervised Domain Adaptation | ||
| [arXiv 28 May 2019] | Adversarial Domain Adaptation Being Aware of Class Relationships | ||
| [arXiv 30 Nov 2018] | Domain-Invariant Adversarial Learning for Unsupervised Domain Adaption | ||
| [arXiv 17 Feb 2019] | Unsupervised Domain Adaptation using Deep Networks with Cross-Grafted Stacks | ||
| [arXiv 30 Dec 2018] | DART: Domain-Adversarial Residual-Transfer Networks for Unsupervised Cross-Domain Image Classification | ||
| [arXiv 2 Dec 2018] | Unsupervised Domain Adaptation using Generative Models and Self-ensembling | ||
| [arXiv 10 Oct 2018] | Domain Confusion with Self Ensembling for Unsupervised Adaptation | ||
| [arXiv 10 Sep 2018] | Improving Adversarial Discriminative Domain Adaptation | ||
| [arXiv 6 Jul 2018] | M-ADDA: Unsupervised Domain Adaptation with Deep Metric Learning | ||
| [arXiv 4 Jun 2018] | Factorized Adversarial Networks for Unsupervised Domain Adaptation | ||
| [arXiv 21 May 2018] | DiDA: Disentangled Synthesis for Domain Adaptation | ||
| [arXiv 25 Apr 2018] | Unsupervised Domain Adaptation with Adversarial Residual Transform Networks | ||
| [arXiv 28 Jun 2018] | Causal Generative Domain Adaptation Networks | ||
Papers / Unsupervised DA / Distance-based Methods | |||
| [TPAMI] | Transferable Representation Learning with Deep Adaptation Networks | ||
| [InfSc2019] | Robust unsupervised domain adaptation for neural networks via moment alignment | ||
| [AAAI2020] | Domain Conditioned Adaptation Network | ||
| [AAAI2020] | HoMM: Higher-order Moment Matching for Unsupervised Domain Adaptation | ||
| [ICCV2019] | Normalized Wasserstein for Mixture Distributions With Applications in Adversarial Learning and Domain Adaptation | ||
| [AAAI2019] | Joint Domain Alignment and Discriminative Feature Learning for Unsupervised Deep Domain Adaptation | ||
| [CVPR2018] | Residual Parameter Transfer for Deep Domain Adaptation | ||
| [AAAI2018] | Deep Asymmetric Transfer Network for Unbalanced Domain Adaptation | ||
| [ICLR2017] | Central Moment Discrepancy for Unsupervised Domain Adaptation , , | ||
| [ECCV2016] | Deep CORAL: Correlation Alignment for Deep Domain Adaptation | ||
| [ICML2015] | Learning Transferable Features with Deep Adaptation Networks | ||
| [NIPS2016] | Unsupervised Domain Adaptation with Residual Transfer Networks | ||
| [ICML2017] | Deep Transfer Learning with Joint Adaptation Networks | ||
| [Arxiv 2014] | Deep Domain Confusion: Maximizing for Domain Invariance | ||
Papers / Unsupervised DA / Information-based Methods | |||
| [AAAI2021] | Hypothesis Disparity Regularized Mutual Information Maximization | ||
Papers / Unsupervised DA / Optimal Transport | |||
| [AISTATS2023] | Global-Local Regularization Via Distributional Robustness | ||
| [UAI2021] | MOST: Multi-Source Domain Adaptation via Optimal Transport for Student-Teacher Learning | ||
| [ICML2021] | LAMDA: Label Matching Deep Domain Adaptation | ||
| [IJCAI2021] | TIDOT: A Teacher Imitation Learning Approach for Domain Adaptation with Optimal Transport | ||
| [ICML2021] | Unbalanced minibatch Optimal Transport; applications to Domain Adaptation | ||
| [ICML2020] | Graph Optimal Transport for Cross-Domain Alignment | ||
| [ICML2020] | Margin-aware Adversarial Domain Adaptation with Optimal Transport | ||
| [IJCAI2020] | Metric Learning in Optimal Transport for Domain Adaptation | ||
| [CVPR2020] | Reliable Weighted Optimal Transport for Unsupervised Domain Adaptation | ||
| [CVPR2020] | Enhanced Transport Distance for Unsupervised Domain Adaptation | ||
| [IJCAI2019] | Differentially Private Optimal Transport: Application to Domain Adaptation | ||
| [ECCV2018] | DeepJDOT: Deep Joint distribution optimal transport for unsupervised domain adaptation | ||
| [NIPS2017] | Joint Distribution Optimal Transportation for Domain Adaptation | ||
| [20 Sep 2019] | CDOT: Continuous Domain Adaptation using Optimal Transport | ||
Papers / Unsupervised DA / Incremental Methods | |||
| [TNNLS 2020] | Incremental Unsupervised Domain-Adversarial Training of Neural Networks | ||
Papers / Unsupervised DA / Semi-Supervised-Learning-Based Methods | |||
| [ECCV2020] | Label Propagation with Augmented Anchors: A Simple Semi-Supervised Learning baseline for Unsupervised Domain Adaptation | ||
| [arXiv 2021] | Semi-supervised Models are Strong Unsupervised Domain Adaptation Learners | ||
Papers / Unsupervised DA / Self-training-Based Methods | |||
| [NeurIPS2021] | Cycle Self-Training for Domain Adaptation | ||
| [ICCV Workshop 2021] | Meta Self-Learning for Multi-Source Domain Adaptation: A Benchmark | ||
| [ECCV 2020] | Instance Adaptive Self-Training for Unsupervised Domain Adaptation | ||
| [NeurIPS 2020] | Self-training Avoids Using Spurious Features Under Domain Shift | ||
| [ECCV2020] | Two-phase Pseudo Label Densification for Self-training based Domain Adaptation | ||
| [arXiv 20211] | Probabilistic Contrastive Learning for Domain Adaptation | ||
| [arXiv 2021] | Gradual Domain Adaptation via Self-Training of Auxiliary Models | ||
Papers / Unsupervised DA / Self-Supervised Methods | |||
| [ECCV2020] | Self-Supervised CycleGAN for Object-Preserving Image-to-Image Domain Adaptation | ||
| [arXiv 26 Sep 2019] | Unsupervised Domain Adaptation through Self-Supervision | ||
Papers / Unsupervised DA / Transformer-based Methods | |||
| [CVPR2022] | Safe Self-Refinement for Transformer-Based Domain Adaptation [ | ||
Papers / Unsupervised DA / Other Methods | |||
| [ECCV2022] | Prior Knowledge Guided Unsupervised Domain Adaptation | ||
| [ACCV2022] | Revisiting Unsupervised Domain Adaptation Models: a Smoothness Perspective | ||
| [NeurIPS2021] | Reducing the Covariate Shift by Mirror Samples in Cross Domain Alignment | ||
| [NeurIPS2021] | Pareto Domain Adaptation | ||
| [NeurIPS2021] | ToAlign: Task-Oriented Alignment for Unsupervised Domain Adaptation | ||
| [NeurIPS2021] | A Prototype-Oriented Framework for Unsupervised Domain Adaptation | ||
| [NeurIPS2021] | Understanding the Limits of Unsupervised Domain Adaptation via Data Poisoning | ||
| [ICCV2021] | SENTRY: Selective Entropy Optimization via Committee Consistency for Unsupervised Domain Adaptation | ||
| [ICCV2021] | Transporting Causal Mechanisms for Unsupervised Domain Adaptation | ||
| [ICCV2021] | Semantic Concentration for Domain Adaptation | ||
| [CVPR2021] | FixBi: Bridging Domain Spaces for Unsupervised Domain Adaptation | ||
| [CVPR2021] | Domain Adaptation With Auxiliary Target Domain-Oriented Classifier | ||
| [CVPR2021] | Conditional Bures Metric for Domain Adaptation | ||
| [CVPR2021] | DRANet: Disentangling Representation and Adaptation Networks for Unsupervised Cross-Domain Adaptation | ||
| [CVPR2021] | Visualizing Adapted Knowledge in Domain Transfer | ||
| [CVPR2021] | Instance Level Affinity-Based Transfer for Unsupervised Domain Adaptation | ||
| [CVPR2021] | Dynamic Domain Adaptation for Efficient Inference | ||
| [CVPR2021] | Transferable Semantic Augmentation for Domain Adaptation | ||
| [CVPR2021] | MetaAlign: Coordinating Domain Alignment and Classification for Unsupervised Domain Adaptation | ||
| [CVPR2021] | DRANet: Disentangling Representation and Adaptation Networks for Unsupervised Cross-Domain Adaptation | ||
| [CVPR2021] | Dynamic Weighted Learning for Unsupervised Domain Adaptation | ||
| [NeurIPS 2020] | Domain Adaptation with Conditional Distribution Matching and Generalized Label Shift | ||
| [NeurIPS 2020] | Transferable Calibration with Lower Bias and Variance in Domain Adaptation | ||
| [NeurIPS 2020] | A Dictionary Approach to Domain-Invariant Learning in Deep Networks | ||
| [NeurIPS2020] | Heuristic Domain Adaptation | ||
| [ECCV2020] | Unsupervised Domain Adaptation for Semantic Segmentation of NIR Images through Generative Latent Search | ||
| [ECCV2020] | Mind the Discriminability: Asymmetric Adversarial Domain Adaptation | ||
| [ECCV2020] | Domain2Vec: Domain Embedding for Unsupervised Domain Adaptation | ||
| [ECCV2020] | CSCL: Critical Semantic-Consistent Learning for Unsupervised Domain Adaptation | ||
| [ECCV2020] | Minimum Class Confusion for Versatile Domain Adaptation | ||
| [ECCV2020] | Partially-Shared Variational Auto-encoders for Unsupervised Domain Adaptation with Target Shift | ||
| [ECCV2020] | Label Propagation with Augmented Anchors: A Simple Semi-Supervised Learning baseline for Unsupervised Domain Adaptation | ||
| [CVPR2020 Oral] | Unsupervised Domain Adaptation via Structurally Regularized Deep Clustering | ||
| [CVPR2020 Oral] | Towards Discriminability and Diversity: Batch Nuclear-norm Maximization under Label Insufficient Situations | ||
| [CVPR2020] | Unsupervised Domain Adaptation With Hierarchical Gradient Synchronization | ||
| [CVPR2020] | Spherical Space Domain Adaptation With Robust Pseudo-Label Loss | ||
| [CVPR2020] | Stochastic Classifiers for Unsupervised Domain Adaptation | ||
| [CVPR2020] | Structure Preserving Generative Cross-Domain Learning | ||
| [CVPR2020] | Light-weight Calibrator: A Separable Component for Unsupervised Domain Adaptation | ||
| [ICLR2020] | Domain Adaptive Multiflow Networks | ||
| [AAAI2020] | Unsupervised Domain Adaptation via Discriminative Manifold Embedding and Alignment | ||
| [Paper] | Visual Domain Adaptation by Consensus-based Transfer to Intermediate Domain | ||
| [AAAI2020] | Unsupervised Domain Adaptation via Structured Prediction Based Selective Pseudo-Labeling | ||
| [ICDM2019] | CUDA: Contradistinguisher for Unsupervised Domain Adaptation | ||
| [ICML2019] | Domain Adaptation with Asymmetrically-Relaxed Distribution Alignment | ||
| [ICCV2019] | Batch Weight for Domain Adaptation With Mass Shift | ||
| [ICCV2019] | Switchable Whitening for Deep Representation Learning | ||
| [ICCV2019 Oral] | Confidence Regularized Self-Training | ||
| [ICCV2019] | Larger Norm More Transferable: An Adaptive Feature Norm Approach for Unsupervised Domain Adaptation | ||
| [CVPR2019(Oral)] | Transferrable Prototypical Networks for Unsupervised Domain Adaptation | ||
| [CVPR2019] | Sliced Wasserstein Discrepancy for Unsupervised Domain Adaptation | ||
| [CVPR 2019] | Unsupervised Domain Adaptation using Feature-Whitening and Consensus Loss | ||
| [CVPR2019] | Domain Specific Batch Normalization for Unsupervised Domain Adaptation | ||
| [CVPR2019] | AdaGraph: Unifying Predictive and Continuous Domain Adaptation through Graphs | ||
| [CVPR2019] | Unsupervised Visual Domain Adaptation: A Deep Max-Margin Gaussian Process Approach | ||
| [CVPR2019] | Contrastive Adaptation Network for Unsupervised Domain Adaptation | ||
| [CVPR2019] | Distant Supervised Centroid Shift: A Simple and Efficient Approach to Visual Domain Adaptation | ||
| [CVPRW2019] | Unsupervised Domain Adaptation via Calibrating Uncertainties | ||
| [IJCAI2019] | Bayesian Uncertainty Matching for Unsupervised Domain Adaptation | ||
| [ICLR2019] | Unsupervised Domain Adaptation for Distance Metric Learning | ||
| [NIPS2018] | Co-regularized Alignment for Unsupervised Domain Adaptation | ||
| [TIP 2018] | Domain Invariant and Class Discriminative Feature Learning for Visual Domain Adaptation | ||
| [ECCV2018] | Graph Adaptive Knowledge Transfer for Unsupervised Domain Adaptation | ||
| [CVPR2018] | Aligning Infinite-Dimensional Covariance Matrices in Reproducing Kernel Hilbert Spaces for Domain Adaptation | ||
| [AAAI2018] | Unsupervised Domain Adaptation with Distribution Matching Machines | ||
| [ICLR2018] | Learning to cluster in order to transfer across domains and tasks | ||
| [ICLR2018] | Self-Ensembling for Visual Domain Adaptation | ||
| [ICLR2018] | Minimal-Entropy Correlation Alignment for Unsupervised Deep Domain Adaptation | ||
| [ICCV2017] | Associative Domain Adaptation | ||
| [ICCV2017] | AutoDIAL: Automatic DomaIn Alignment Layers | ||
| [ICML2017] | Asymmetric Tri-training for Unsupervised Domain Adaptation | ||
| [NIPS2016] | Learning Transferrable Representations for Unsupervised Domain Adaptation | ||
| [IEEE TMI 2020] | Target-Independent Domain Adaptation for WBC Classification using Generative Latent Search | ||
| [Pattern Recognition(2018)] | Adaptive Batch Normalization for practical domain adaptation | ||
| [IEEE ACCESS] | Unsupervised Domain Adaptation by Mapped Correlation Alignment | ||
| [6 Feb 2022] | Low-confidence Samples Matter for Domain Adaptation | ||
| [21 Nov 2019] | Improving Unsupervised Domain Adaptation with Variational Information Bottleneck | ||
| [28 Oct 2019] | Deep causal representation learning for unsupervised domain adaptation | ||
| [25 Sep 2019] | Domain-invariant Learning using Adaptive Filter Decomposition | ||
| [arXiv 30 May 2019] | Discriminative Clustering for Robust Unsupervised Domain Adaptation | ||
| [arXiv on 24 May 2019] | Virtual Mixup Training for Unsupervised Domain Adaptation | ||
| [arXiv 26 May 2019] | Learning Smooth Representation for Unsupervised Domain Adaptation | ||
| [arXiv 13 Apr 2019] | Towards Self-similarity Consistency and Feature Discrimination for Unsupervised Domain Adaptation | ||
| [arXiv 2 Apr 2019] | Easy Transfer Learning By Exploiting Intra-domain Structures | ||
| [arXiv 30 Jan 2019] | Domain Discrepancy Measure Using Complex Models in Unsupervised Domain Adaptation | ||
| [arXiv 22 Jan 2019] | Domain Alignment with Triplets | ||
| [arXiv 17 Nov 2018] | Deep Discriminative Learning for Unsupervised Domain Adaptation | ||
Papers / Foundation-Models based DA | |||
| [ICML2023] | POUF: Prompt-oriented unsupervised fine-tuning for large pre-trained models | ||
Papers / Semi-supervised DA | |||
| [CVPR 2023] | Semi-Supervised Domain Adaptation With Source Label Adaptation | ||
| [IJCAI 2022] | Multi-level Consistency Learning for Semi-supervised Domain Adaptation | ||
| [ICLR 2022] | AdaMatch: A Unified Approach to Semi-Supervised Learning and Domain Adaptation | ||
| [NeurIPS] | CLDA: Contrastive Learning for Semi-Supervised Domain Adaptation | ||
| [ICCV2021] | Deep Co-Training With Task Decomposition for Semi-Supervised Domain Adaptation | ||
| [ICCV2021] | ECACL: A Holistic Framework for Semi-Supervised Domain Adaptation | ||
| [CVPR2021] | Cross-Domain Adaptive Clustering for Semi-Supervised Domain Adaptation | ||
| [CVPR2021] | Semi-supervised Domain Adaptation based on Dual-level Domain Mixing for Semantic Segmentation | ||
| [CVPR2021] | Learning Invariant Representations and Risks for Semi-supervised Domain Adaptation | ||
| [CVPRW2021] | Improving Semi-Supervised Domain Adaptation Using Effective Target Selection and Semantics | ||
| [ECCV2020] | Attract, Perturb, and Explore: Learning a Feature Alignment Network for Semi-supervised Domain Adaptation | ||
| [ECCV2020] | Online Meta-Learning for Multi-Source and Semi-Supervised Domain Adaptation | ||
| [IJCAI2020] | Bidirectional Adversarial Training for Semi-Supervised Domain Adaptation | ||
| [ICCV2019] | Semi-supervised Domain Adaptation via Minimax Entropy | ||
| [Neural Networks] | Context-guided entropy minimization for semi-supervised domain adaptation | ||
| [22 Nov 2022] | Pred&Guide: Labeled Target Class Prediction for Guiding Semi-Supervised Domain Adaptation | ||
| [ 24 Jul 2020] | MiCo: Mixup Co-Training for Semi-Supervised Domain Adaptation | ||
| [6 Feb 2020] | Opposite Structure Learning for Semi-supervised Domain Adaptation | ||
| [25 Oct 2019] | Reducing Domain Gap via Style-Agnostic Networks | ||
Papers / Weakly-Supervised DA | |||
| [IJCAI2020] | Towards Accurate and Robust Domain Adaptation under Noisy Environments | ||
| [CVPR2019] | Weakly Supervised Open-set Domain Adaptation by Dual-domain Collaboration | ||
| [AAAI2019] | Transferable Curriculum for Weakly-Supervised Domain Adaptation | ||
| [arXiv on 19 May 2019] | Butterfly: Robust One-step Approach towards Wildly-unsupervised Domain Adaptation | ||
Papers / Zero-shot DA | |||
| [ICCV2021] | Collaborative Learning With Disentangled Features for Zero-Shot Domain Adaptation | ||
| [ICCV2021] | Zero-Shot Day-Night Domain Adaptation with a Physics Prior | ||
| [ECCV2020] | High Resolution Zero-Shot Domain Adaptation of Synthetically Rendered Face Images | ||
| [ECCV2020] | Adversarial Learning for Zero-shot Domain Adaptation | ||
| [ECCV2020] | HGNet: Hybrid Generative Network for Zero-shot Domain Adaptation | ||
| [ACML2019] | Zero-shot Domain Adaptation Based on Attribute Information | ||
| [ICCV2019] | Conditional Coupled Generative Adversarial Networks for Zero-Shot Domain Adaptation | ||
| [NIPS2018] | Generalized Zero-Shot Learning with Deep Calibration Network | ||
| [ECCV2018] | Zero-Shot Deep Domain Adaptation | ||
Papers / One-shot DA | |||
| [NeurIPS2020] | Adversarial Style Mining for One-Shot Unsupervised Domain Adaptation | ||
| [ICLR Workshop 2014] | One-Shot Adaptation of Supervised Deep Convolutional Models | ||
| [arxiv] | One-Shot Imitation from Observing Humans via Domain-Adaptive Meta-Learning | ||
Papers / Few-shot UDA | |||
| [CVPR2021] | Prototypical Cross-domain Self-supervised Learning for Few-shot Unsupervised Domain Adaptation | ||
| [arXiv 18 Mar 2020] | Cross-domain Self-supervised Learning for Domain Adaptation with Few Source Labels | ||
Papers / Few-shot DA | |||
| [WACV2021] | Domain-Adaptive Few-Shot Learning | ||
| [ICML2020] | Few-shot Domain Adaptation by Causal Mechanism Transfer | ||
| [CVPR2019] | Few-Shot Adaptive Faster R-CNN | ||
| [CVPR2019 Oral] | d-SNE: Domain Adaptation using Stochastic Neighborhood Embedding | ||
| [NIPS2017] | Few-Shot Adversarial Domain Adaptation | ||
| [arXiv 18 May 2020] | Feature transformation ensemble model with batch spectral regularization for cross-domain few-shot classification | ||
| [arXiv 8 June 2020] | Ensemble model with batch spectral regularization and data blending for cross-domain few-shot learning with unlabeled data | ||
Papers / Partial DA | |||
| [NeurIPS2021] | Implicit Semantic Response Alignment for Partial Domain Adaptation | ||
| [NeurIPS2021] | Adversarial Reweighting for Partial Domain Adaptation | ||
| [ECCV2020] | A Balanced and Uncertainty-aware Approach for Partial Domain Adaptation | ||
| [ECCV2020] | Discriminative Partial Domain Adversarial Network | ||
| [CVPR2020] | Selective Transfer With Reinforced Transfer Network for Partial Domain Adaptation | ||
| [ACM MM2020] | Adaptively-Accumulated Knowledge Transfer for Partial Domain Adaptation | ||
| [BMVC2019] | Multi-Weight Partial Domain Adaptation | ||
| [CVPR2019] | Learning to Transfer Examples for Partial Domain Adaptation | ||
| [ECCV2018] | Partial Adversarial Domain Adaptation | ||
| [CVPR2018] | Importance Weighted Adversarial Nets for Partial Domain Adaptation | ||
| [CVPR2018] | Partial Transfer Learning with Selective Adversarial Networks | ||
| [TPAMI2020] | Unsupervised Multi-Class Domain Adaptation: Theory, Algorithms, and Practice | ||
| [arXiv 06 Dec 2020] | Select, Label, and Mix: Learning Discriminative Invariant Feature Representations for Partial Domain Adaptation | ||
| [20 Feb 2020] | Unsupervised Multi-Class Domain Adaptation: Theory, Algorithms, and Practice | ||
| [arXiv 12 Jun 2019] | Tackling Partial Domain Adaptation with Self-Supervision | ||
| [arXiv 10 May 2019] | Domain Adversarial Reinforcement Learning for Partial Domain Adaptation | ||
Papers / Open Set DA | |||
| [ICCV2021] | Towards Novel Target Discovery Through Open-Set Domain Adaptation | ||
| [ECCV2020] | On the Effectiveness of Image Rotation for Open Set Domain Adaptation | ||
| [ECCV2020] | Multi-Source Open-Set Deep Adversarial Domain Adaptation | ||
| [ICML2020] | Progressive Graph Learning for Open-Set Domain Adaptation | ||
| [IJCAI2020] | Joint Partial Optimal Transport for Open Set Domain Adaptation | ||
| [CVPR2020] | Exploring Category-Agnostic Clusters for Open-Set Domain Adaptation | ||
| [CVPR 2020] | Towards Inheritable Models for Open-Set Domain Adaptation | ||
| [ICCV2019] | Attract or Distract: Exploit the Margin of Open Set | ||
| [CVPR2019] | Separate to Adapt: Open Set Domain Adaptation via Progressive Separation | ||
| [CVPR2019] | Weakly Supervised Open-set Domain Adaptation by Dual-domain Collaboration | ||
| [ICLR2019] | Learning Factorized Representations for Open-set Domain Adaptation | ||
| [ECCV2018] | Open Set Domain Adaptation by Backpropagation | ||
| [ICCV2017] | Open Set Domain Adaptation | ||
| [Applied Intelligence 2022] | Open-set domain adaptation by deconfounding domain gaps | ||
| [TPAMI2020] | Unsupervised Multi-Class Domain Adaptation: Theory, Algorithms, and Practice | ||
| [IEEE TMM] | Adversarial Network with Multiple Classifiers for Open Set Domain Adaptation | ||
| [10 Feb 2020] | Collaborative Training of Balanced Random Forests for Open Set Domain Adaptation | ||
| [3 May 2019] | Known-class Aware Self-ensemble for Open Set Domain Adaptation | ||
Papers / Universal DA | |||
| [EMNLP 2023 Findings] | Universal Domain Adaptation for Robust Handling of Distributional Shifts in NLP | ||
| [NeurIPS2022] | Subsidiary Prototype Alignment for Universal Domain Adaptation | ||
| [ICCV2021] | OVANet: One-vs-All Network for Universal Domain Adaptation | ||
| [ICCV 2021] | Active Universal Domain Adaptation | ||
| [CVPR 2021] | Domain Consensus Clustering for Universal Domain Adaptation | ||
| [CVPR2021] | Divergence Optimization for Noisy Universal Domain Adaptation | ||
| [NeurIPS 2020] | Universal Domain Adaptation through Self Supervision | ||
| [ECCV2020] | Learning to Detect Open Classes for Universal Domain Adaptation | ||
| [CVPR2020] | Universal Source-Free Domain Adaptation | ||
| [CVPR2019] | Universal Domain Adaptation | ||
| [Applied Intelligence 2023] | Universal Model Adaptation by Style Augmented Open-set Consistency | ||
| [5 Nov 2020] | Universal Multi-Source Domain Adaptation | ||
| [14 Jan 2020] | A Sample Selection Approach for Universal Domain Adaptation | ||
Papers / Open Compound DA | |||
| [NeurIPS2020] | Discover, Hallucinate, and Adapt: Open Compound Domain Adaptation for Semantic Segmentation | ||
| [CVRP2020 Oral] | Open Compound Domain Adaptation | ||
| [TCSVT 2022] | Source-Free Open Compound Domain Adaptation in Semantic Segmentation | ||
Papers / Multi Source DA | |||
| [NeurIPS2021] | Confident Anchor-Induced Multi-Source Free Domain Adaptation | ||
| [ICCV2021] | mDALU: Multi-Source Domain Adaptation and Label Unification With Partial Datasets | ||
| [ICCV2021] | STEM: An Approach to Multi-Source Domain Adaptation With Guarantees | ||
| [ICCV2021] | T-SVDNet: Exploring High-Order Prototypical Correlations for Multi-Source Domain Adaptation | ||
| [ICCV2021] | Multi-Source Domain Adaptation for Object Detection | ||
| [ICCV2021] | Information-Theoretic Regularization for Multi-Source Domain Adaptation | ||
| [CVPR2021] | Partial Feature Selection and Alignment for Multi-Source Domain Adaptation | ||
| [CVPR2021] | Wasserstein Barycenter for Multi-Source Domain Adaptation | ||
| [CVPR2021] | Unsupervised Multi-source Domain Adaptation Without Access to Source Data | ||
| [CVPR2021] | Dynamic Transfer for Multi-Source Domain Adaptation | ||
| [CVPR2021] | Multi-Source Domain Adaptation with Collaborative Learning for Semantic Segmentation | ||
| [UAI2021] | MOST: Multi-Source Domain Adaptation via Optimal Transport for Student-Teacher Learning | ||
| [ICCV Workshop 2021] | Meta Self-Learning for Multi-Source Domain Adaptation: A Benchmark | ||
| [NeurIPS 2020] | Your Classifier can Secretly Suffice Multi-Source Domain Adaptation | ||
| [ECCV2020] | Multi-Source Open-Set Deep Adversarial Domain Adaptation | ||
| [ECCV2020] | Online Meta-Learning for Multi-Source and Semi-Supervised Domain Adaptation | ||
| [ECCV2020] | Multi-Source Open-Set Deep Adversarial Domain Adaptation | ||
| [ECCV2020] | Curriculum Manager for Source Selection in Multi-Source Domain Adaptation | ||
| [ICML2020] | Domain Aggregation Networks for Multi-Source Domain Adaptation | ||
| [ECCV2020] | 69 | over 4 years ago | Learning to Combine: Knowledge Aggregation for Multi-Source Domain Adaptation |
| [AAAI2020] | Multi-Source Domain Adaptation for Text Classification via DistanceNet-Bandits | ||
| [AAAI2020] | Adversarial Training Based Multi-Source Unsupervised Domain Adaptation for Sentiment Analysis | ||
| [AAAI2020] | Multi-source Domain Adaptation for Visual Sentiment Classification | ||
| [AAAI2020] | Multi-source Distilling Domain Adaptation | ||
| [NeurlPS2019] | Multi-source Domain Adaptation for Semantic Segmentation | ||
| [ICCV2019] | Moment Matching for Multi-Source Domain Adaptation | ||
| [ICLR2019] | Multi-Domain Adversarial Learning | ||
| [NIPS2018] | Algorithms and Theory for Multiple-Source Adaptation | ||
| [NIPS2018] | Adversarial Multiple Source Domain Adaptation | ||
| [CVPR2018] | Boosting Domain Adaptation by Discovering Latent Domains | ||
| [CVPR2018] | Deep Cocktail Network: Multi-source Unsupervised Domain Adaptation with Category Shift | ||
| [TPAMI 2022] | Graphical Modeling for Multi-Source Domain Adaptation | ||
| [Cognitive Computation] | Unsupervised sentiment analysis by transferring multi-source knowledge | ||
| [Information Fusion] | A survey of multi-source domain adaptation | ||
| [arXiv] | Mutual learning network for multi-source domain adaptation | ||
| [arXiv] | Domain Adaptive Ensemble Learning | ||
| [14 Oct 2019] | Multi-Source Domain Adaptation and Semi-Supervised Domain Adaptation with Focus on Visual Domain Adaptation Challenge 2019 | ||
Papers / Multi Target DA | |||
| [WACV2022] | CoNMix for Source-free Single and Multi-target Domain Adaptation | ||
| [CVPR2021] | Curriculum Graph Co-Teaching for Multi-Target Domain Adaptation | ||
| [CVPR2021] | Multi-Target Domain Adaptation with Collaborative Consistency Learning | ||
| [arXiv] | Unsupervised Multi-Target Domain Adaptation: An Information Theoretic Approach | ||
Papers / Incremental DA | |||
| [NeurIPS2021] | Lifelong Domain Adaptation via Consolidated Internal Distribution | ||
| [CVPR2021] | Continual Adaptation of Visual Representations via Domain Randomization and Meta-learning | ||
| [CVPR2021] | ConDA: Continual Unsupervised Domain Adaptation | ||
| [AAAI2021] | Gradient Regularized Contrastive Learning for Continual Domain Adaptation | ||
| [NeurIPS2021] | Gradual Domain Adaptation without Indexed Intermediate Domains | ||
| [NeurIPS 2020] | Learning to Adapt to Evolving Domains | ||
| [ECCV2020] | Class-Incremental Domain Adaptation | ||
| [ICRA2018] | Incremental Adversarial Domain Adaptation for Continually Changing Environments | ||
| [CVPR2014] | Continuous Manifold based Adaptation for Evolving Visual Domains | ||
Papers / Multi Step DA | |||
| [arXiv] | Adversarial Domain Adaptation for Stance Detection | ||
| [arXiv] | Ensemble Adversarial Training: Attacks and Defenses | ||
| [AAAI2017] | Distant domain transfer learning | ||
Papers / Heterogeneous DA | |||
| [IJCAI2020] | Domain Adaptive Classification on Heterogeneous Information Networks | ||
| [ACM MM2019] | Heterogeneous Domain Adaptation via Soft Transfer Network | ||
Papers / Target-agnostic DA | |||
| [8 Sep 2019] | Compound Domain Adaptation in an Open World | ||
| [ICML2019] | Domain Agnostic Learning with Disentangled Representations | ||
| [CVPR2019] | Blending-target Domain Adaptation by Adversarial Meta-Adaptation Networks | ||
Papers / Federated DA | |||
| [5 Nov 2019] | Federated Adversarial Domain Adaptation | ||
Papers / Continuously Indexed DA | |||
| [ICML 2020] | Continuously Indexed Domain Adaptation | ||
Papers / Source Free DA | |||
| [AAAI2023] | Domain Adaptation with Adversarial Training on Penultimate Activations | ||
| [ICCV2023] | Source-free Domain Adaptive Human Pose Estimation | ||
| [IJCAI2023] | RAIN: RegulArization on Input and Network for Black-Box Domain Adaptation | ||
| [WACV2022] | CoNMix for Source-free Single and Multi-target Domain Adaptation | ||
| [ECCV2022] | Source-free Video Domain Adaptation by Learning Temporal Consistency for Action Recognition | ||
| [ECCV2022] | Concurrent Subsidiary Supervision for Unsupervised Source-Free Domain Adaptation | ||
| [ICML2022] | Balancing Discriminability and Transferability for Source-Free Domain Adaptation | ||
| [IJCAI2021] | Source-free Domain Adaptation via Avatar Prototype Generation and Adaptation | ||
| [NeurIPS2021] | Confident Anchor-Induced Multi-Source Free Domain Adaptation | ||
| [NeurIPS2021] | Model Adaptation: Historical Contrastive Learning for Unsupervised Domain Adaptation without Source Data | ||
| [NeurIPS2021] | Exploiting the Intrinsic Neighborhood Structure for Source-free Domain Adaptation | ||
| [BMVC2021] | Unsupervised Domain Adaptation of Black-Box Source Models | ||
| [ICCV2021] | Generalize Then Adapt: Source-Free Domain Adaptive Semantic Segmentation | ||
| [ICCV2021] | Generalized Source-free Domain Adaptation | ||
| [ICCV2021] | Adaptive Adversarial Network for Source-free Domain Adaptation | ||
| [CVPR2021] | Visualizing Adapted Knowledge in Domain Transfer | ||
| [CVPR2021] | Unsupervised Multi-source Domain Adaptation Without Access to Source Data | ||
| [CVPR2021] | Source-Free Domain Adaptation for Semantic Segmentation | ||
| [WACV2021] | Domain Impression: A Source Data Free Domain Adaptation Method | ||
| [CVPR2020] | Model Adaptation: Unsupervised Domain Adaptation Without Source Data | ||
| [CVPR2020] | Universal Source-Free Domain Adaptation | ||
| [CVPR2020] | Towards Inheritable Models for Open-Set Domain Adaptation | ||
| [ICML2020] | Do We Really Need to Access the Source Data? Source Hypothesis Transfer for Unsupervised Domain Adaptation | ||
| [7 Jul 2021] | Learning Invariant Representation with Consistency and Diversity for Semi-supervised Source Hypothesis Transfer | ||
| [14 Dec 2020] | Source Data-absent Unsupervised Domain Adaptation through Hypothesis Transfer and Labeling Transfer | ||
Papers / Active DA | |||
| [ICCV2023] | Local Context-Aware Active Domain Adaptation | ||
| [WACV2023] | Reducing Annotation Effort by Identifying and Labeling Contextually Diverse Classes for Semantic Segmentation Under Domain Shift | ||
| [CVPR2022] | Towards Fewer Annotations: Active Learning via Region Impurity and Prediction Uncertainty for Domain Adaptive Semantic Segmentation | ||
| [AAAI2022] | Active Learning for Domain Adaptation: An Energy-based Approach | ||
| [ICCV2021] | Multi-Anchor Active Domain Adaptation for Semantic Segmentation | ||
| [ICCV2021] | Active Domain Adaptation via Clustering Uncertainty-Weighted Embeddings | ||
| [ICCV2021] | Active Universal Domain Adaptation | ||
| [ICCV2021] | S3VAADA: Submodular Subset Selection for Virtual Adversarial Active Domain Adaptation | ||
| [CVPR2021] | Transferable Query Selection for Active Domain Adaptation | ||
Papers / Generalized Domain Adaptation | |||
| [CVPR2021] | Generalized Domain Adaptation | ||
Papers / Model Selection | |||
| [ICLR2023ORAL] | Addressing Parameter Choice Issues in Unsupervised Domain Adaptation by Aggregation | ||
| [NeurIPS2021] | The Balancing Principle for Parameter Choice in Distance-Regularized Domain Adaptation | ||
| [ICML2019] | Towards Accurate Model Selection in Deep Unsupervised Domain Adaptation | ||
Papers / Other Transfer Learning Paradigms / Domain Generalization | |||
| [ICLR2024] | Adapting to Distribution Shift by Visual Domain Prompt Generation | ||
| [AAAI2024 (Oral)] | Test-Time Domain Adaptation by Learning Domain-Aware Batch Normalization | ||
| [CVPR 2024] | A Dual-Augmentor Framework for Domain Generalization in 3D Human Pose Estimation | ||
| [CVPR 2024] | Improving Single Domain-Generalized Object Detection: A Focus on Diversification and Alignment | ||
| [WACV 2024] | Generalization by Adaptation: Diffusion-Based Domain Extension for Domain-Generalized Semantic Segmentation | ||
| [ICLR 2023] | Topology-aware Robust Optimization for Out-of-Distribution Generalization | ||
| [ICCV Workshop 2023] | A Re-Parameterized Vision Transformer (ReVT) for Domain-Generalized Semantic Segmentation | ||
| [ICCV Workshop 2023] | Weight Averaging Improves Knowledge Distillation under Domain Shift | ||
| [AAAI2023 oral] | Adaptive Texture Filtering for Single-Domain Generalized Segmentation | ||
| [ICCV2023] | PromptStyler: Prompt-driven Style Generation for Source-free Domain Generalization | ||
| [ICLR2023(Oral)] | Sparse Mixture-of-Experts are Domain Generalizable Learners | ||
| [NeruIPS2022] | Meta-DMoE: Adapting to Domain Shift by Meta-Distillation from Mixture-of-Experts | ||
| [ECCV 2022] | Style-Hallucinated Dual Consistency Learning for Domain Generalized Semantic Segmentation | ||
| [CVPR 2021] | Learning to Generalize Unseen Domains via Memory-based Multi-Source Meta-Learning for Person Re-Identification | ||
| [CVPR2021] | Domain Generalization via Inference-time Label-Preserving Target Projections | ||
| [NeurIPS2020] | Domain Generalization via Entropy Regularization | ||
| [NeurIPS2020] | Domain Generalization for Medical Imaging Classification with Linear-Dependency Regularization | ||
| [ECCV2020] | Learning to Learn with Variational Information Bottleneck for Domain Generalization | ||
| [ECCV2020] | Self-Challenging Improves Cross-Domain Generalization | ||
| [ECCV2020] | Learning from Extrinsic and Intrinsic Supervisions for Domain Generalization | ||
| [ECCV2020] | Learning to Balance Specificity and Invariance for In and Out of Domain Generalization | ||
| [ECCV2020] | Learning to Generate Novel Domains for Domain Generalization | ||
| [ECCV2020] | Learning to Optimize Domain Specific Normalization for Domain Generalization | ||
| [ECCV2020] | Towards Recognizing Unseen Categories in Unseen Domains | ||
| [ICML2020] | Efficient Domain Generalization via Common-Specific Low-Rank Decomposition | ||
| [CVPR2020] | Learning to Learn Single Domain Generalization | ||
| [ICLR2020] | Generalized Convolutional Forest Networks for Domain Generalization and Visual Recognition | ||
| [ICLR2020] | Cross-Domain Few-Shot Classification via Learned Feature-Wise Transformation | ||
| [AAAI2020] | Domain Generalization Using a Mixture of Multiple Latent Domains | ||
| [Paper] | Deep Domain-Adversarial Image Generation for Domain Generalisation | ||
| [NeurIPS2019] | Domain Generalization via Model-Agnostic Learning of Semantic Features | ||
| [ICCV2019 Oral] | Episodic Training for Domain Generalization [Pytorch]]( ) | ||
| [ICML2019] | Feature-Critic Networks for Heterogeneous Domain Generalization | ||
| [CVPR2019 Oral] | Domain Generalization by Solving Jigsaw Puzzles | ||
| [NIPS2018] | MetaReg: Towards Domain Generalization using Meta-Regularization | ||
| [ECCV2018] | Deep Domain Generalization via Conditional Invariant Adversarial Networks | ||
| [CVPR2018] | Domain Generalization with Adversarial Feature Learning | ||
| [IntellManuf2020] | Domain Generalization for Regression | ||
| [Pattern Recognition(2019)] | Correlation-aware Adversarial Domain Adaptation and Generalization | ||
| [8 Dec 2019] | Adversarial Pyramid Network for Video Domain Generalization | ||
| [18 Sep 2019] | Towards Shape Biased Unsupervised Representation Learning for Domain Generalization | ||
| [24 May 2019] | A Generalization Error Bound for Multi-class Domain Generalization | ||
| [29 Apr 2019] | Adversarial Invariant Feature Learning with Accuracy Constraint for Domain Generalization | ||
| [9 Dec 2018] | Beyond Domain Adaptation: Unseen Domain Encapsulation via Universal Non-volume Preserving Models | ||
Papers / Other Transfer Learning Paradigms / Domain Randomization | |||
| [ICCV2019] | DeceptionNet: Network-Driven Domain Randomization | ||
| [ICCV2019] | Domain Randomization and Pyramid Consistency: Simulation-to-Real Generalization Without Accessing Target Domain Data | ||
Papers / Other Transfer Learning Paradigms / Transfer Metric Learning | |||
| [arXiv] | Transfer Metric Learning: Algorithms, Applications and Outlooks | ||
Papers / Other Transfer Learning Paradigms / Knowledge Transfer | |||
| [ICCV2019] | Attention Bridging Network for Knowledge Transfer | ||
| [ICCV2019] | Few-Shot Image Recognition with Knowledge Transfer | ||
Papers / Other Transfer Learning Paradigms / Others | |||
| [ICCV2019] | Learning Across Tasks and Domains | ||
| [ICCV2019] | UM-Adapt: Unsupervised Multi-Task Adaptation Using Adversarial Cross-Task Distillation | ||
| [ICML2019] | Domain Agnostic Learning with Disentangled Representations | ||
| [CVPR2019] | Unsupervised Open Domain Recognition by Semantic Discrepancy Minimization | ||
| [arXiv 13 Dec 2018] | When Semi-Supervised Learning Meets Transfer Learning: Training Strategies, Models and Datasets | ||
Papers / Applications / Object Detection | |||
| [Arxiv 27 May 2021] | Unsupervised Domain Adaptation of Object Detectors: A Survey | ||
| [BMVC2024] | Improving Object Detection via Local-Global Contrastive Learning | ||
| [AAAI2024] | Supervision Interpolation via LossMix: Generalizing Mixup for Object Detection and Beyond | ||
| [CVPR2023] | Instance Relation Graph Guided Source-Free Domain Adaptive Object Detection | ||
| [WACV2023] | Towards Online Domain Adaptive Object Detection [[ | ||
| [ICIP2022] | Mixture of Teacher Experts for Source-Free Domain Adaptive Object Detection | ||
| [CVPR2022] | Towards Robust Adaptive Object Detection under Noisy Annotations | ||
| [CVPR2022] | H FA R-CNN: Holistic and Hierarchical Feature Alignment for Cross-Domain Weakly Supervised Object Detection | ||
| [CVPR2022] | Cross-Domain Adaptive Teacher for Object Detection | ||
| [CVPR2022] | Task-specific Inconsistency Alignment for Domain Adaptive Object Detection | ||
| [CVPR2022] | SIGMA: Semantic-complete Graph Matching for Domain Adaptive Object Detection | ||
| [CVPR2022] | Single-Domain Generalized Object Detection in Urban Scene via Cyclic-Disentangled Self-Distillation | ||
| [CVPR2022] | Target-Relevant Knowledge Preservation for Multi-Source Domain Adaptive Object Detection | ||
| [CVPR2022] | Cross Domain Object Detection by Target-Perceived Dual Branch Distillation | ||
| [ICLR2022] | Decoupled Adaptation for Cross-Domain Object Detection | ||
| [AAAI2022] | SCAN: Cross Domain Object Detection with Semantic Conditioned Adaptation | ||
| [NeurIPS2021] | SSAL: Synergizing between Self-Training and Adversarial Learning for Domain Adaptive Object Detection | ||
| [ICCV2021] | Multi-Source Domain Adaptation for Object Detection | ||
| [ICCV2021] | Knowledge Mining and Transferring for Domain Adaptive Object Detection | ||
| [ICCV2021] | Dual Bipartite Graph Learning: A General Approach for Domain Adaptive Object Detection | ||
| [ICCV2021] | Seeking Similarities over Differences: Similarity-based Domain Alignment for Adaptive Object Detection | ||
| [CVPR2021] | Informative and Consistent Correspondence Mining for Cross-Domain Weakly Supervised Object Detection | ||
| [CVPR2021] | MeGA-CDA: Memory Guided Attention for Category-Aware Unsupervised Domain Adaptive Object Detection | ||
| [CVPR2021] | SRDAN: Scale-aware and Range-aware Domain Adaptation Network for Cross-dataset 3D Object Detection | ||
| [CVPR2021] | I3Net: Implicit Instance-Invariant Network for Adapting One-Stage Object Detectors | ||
| [CVPR2021] | RPN Prototype Alignment for Domain Adaptive Object Detector | ||
| [CVPR2021] | ST3D: Self-training for Unsupervised Domain Adaptation on 3D ObjectDetection | ||
| [CVPR2021] | Domain-Specific Suppression for Adaptive Object Detection | ||
| [CVPR2021] | Unbiased Mean Teacher for Cross-Domain Object Detection | ||
| [ECCV2020] | YOLO in the Dark - Domain Adaptation Method for Merging Multiple Models | ||
| [ECCV2020] | Collaborative Training between Region Proposal Localization and Classification for Domain Adaptive Object Detection | ||
| [ECCV2020] | One-Shot Unsupervised Cross-Domain Detection | ||
| [ECCV2020] | Every Pixel Matters: Center-aware Feature Alignment for Domain Adaptive Object Detector | ||
| [ECCV2020] | Adapting Object Detectors with Conditional Domain Normalization | ||
| [ECCV2020] | Prior-based Domain Adaptive Object Detection for Hazy and Rainy Conditions | ||
| [ECCV2020] | Domain Adaptive Object Detection via Asymmetric Tri-way Faster-RCNN | ||
| [CVPR2020] | Cross-domain Object Detection through Coarse-to-Fine Feature Adaptation | ||
| [CVPR2020] | Harmonizing Transferability and Discriminability for Adapting Object Detectors | ||
| [CVPR2020] | Exploring Categorical Regularization for Domain Adaptive Object Detection | ||
| [CVPR2020 Oral] | Cross-domain Detection via Graph-induced Prototype Alignment | ||
| [Paper] | Multi-spectral Salient Object Detection by Adversarial Domain Adaptation | ||
| [ICIP2020] | Deep Domain Adaptive Object Detection: a Survey | ||
| [WACV] | Progressive Domain Adaptation for Object Detection | ||
| [IJCNN2019 Oral] | Cross-Domain Car Detection Using Unsupervised Image-to-Image Translation: From Day to Night | ||
| [ICCV2019 Oral] | Self-Training and Adversarial Background Regularization for Unsupervised Domain Adaptive One-Stage Object Detection | ||
| [ICCV2019] | A Robust Learning Approach to Domain Adaptive Object Detection | ||
| [ICCV2019] | Multi-adversarial Faster-RCNN for Unrestricted Object Detection | ||
| [CVPR2019] | Few-Shot Adaptive Faster R-CNN | ||
| [CVPR2019] | Exploring Object Relation in Mean Teacher for Cross-Domain Detection | ||
| [CVPR2019] | Adapting Object Detectors via Selective Cross-Domain Alignment | ||
| [CVPR2019] | Automatic adaptation of object detectors to new domains using self-training | ||
| [CVPR2019] | Towards Universal Object Detection by Domain Attention | ||
| [CVPR2019] | Strong-Weak Distribution Alignment for Adaptive Object Detection | ||
| [CVPR2019] | Diversify and Match: A Domain Adaptive Representation Learning Paradigm for Object Detection | ||
| [CVPR2018] | Cross-Domain Weakly-Supervised Object Detection Through Progressive Domain Adaptation | ||
| [CVPR2018] | Domain Adaptive Faster R-CNN for Object Detection in the Wild | ||
| [ESWA] | Cross-domain object detection using unsupervised image translation | ||
| [Neurocomputing] | Pixel and feature level based domain adaptation for object detection in autonomous driving | ||
| [17 Nov 2021] | See Eye to Eye: A Lidar-Agnostic 3D Detection Framework for Unsupervised Multi-Target Domain Adaptation | ||
| [3 Feb 2020] | Unsupervised Domain Adaptive Object Detection using Forward-Backward Cyclic Adaptation | ||
| [29 Nov 2019] | Prior-based Domain Adaptive Object Detection for Adverse Weather Conditions | ||
| [17 Nov 2019] | Unsupervised Domain Adaptation for Object Detection via Cross-Domain Semi-Supervised Learning | ||
| [15 Nov 2019] | Curriculum Self-Paced Learning for Cross-Domain Object Detection | ||
| [6 Nov 2019] | SCL: Towards Accurate Domain Adaptive Object Detection via Gradient Detach Based Stacked Complementary Losses | ||
Papers / Applications / Semantic Segmentation | |||
| [ACM MM2023] | PiPa: Pixel- and Patch-wise Self-supervised Learning for Domain Adaptative Semantic Segmentation | ||
| [WACV 2023] | Refign: Align and Refine for Adaptation of Semantic Segmentation to Adverse Conditions | ||
| [NeruIPS 2022] | Deliberated Domain Bridging for Domain Adaptive Semantic Segmentation | ||
| [ECCV 2022] | DecoupleNet: Decoupled Network for Domain Adaptive Semantic Segmentation | ||
| [ECCV 2022] | HRDA: Context-Aware High-Resolution Domain-Adaptive Semantic Segmentation | ||
| [ECCV 2022] | Style-Hallucinated Dual Consistency Learning for Domain Generalized Semantic Segmentation | ||
| [CVPR 2022] | DAFormer: Improving Network Architectures and Training Strategies for Domain-Adaptive Semantic Segmentation | ||
| [WACV 2022] | Plugging Self-Supervised Monocular Depth into Unsupervised Domain Adaptation for Semantic Segmentation | ||
| [WACV 2022] | Shallow Features Guide Unsupervised Domain Adaptation for Semantic Segmentation at Class Boundaries | ||
| [NeurIPS2021] | Learning to Adapt via Latent Domains for Adaptive Semantic Segmentation | ||
| [ICCV2021] | Dual Path Learning for Domain Adaptation of Semantic Segmentation | ||
| [ICCV2021] | Exploring Robustness of Unsupervised Domain Adaptation in Semantic Segmentation | ||
| [ICCV2021] | Multi-Anchor Active Domain Adaptation for Semantic Segmentation | ||
| [ICCV2021] | LabOR: Labeling Only if Required for Domain Adaptive Semantic Segmentation | ||
| [ICCV2021] | Self-Mutating Network for Domain Adaptive Segmentation in Aerial Images | ||
| [ICCV2021] | Geometric Unsupervised Domain Adaptation for Semantic Segmentation | ||
| [ICCV2021] | Multi-Target Adversarial Frameworks for Domain Adaptation in Semantic Segmentation | ||
| [ICCV2021] | BAPA-Net: Boundary Adaptation and Prototype Alignment for Cross-Domain Semantic Segmentation | ||
| [ICCV2021] | BiMaL: Bijective Maximum Likelihood Approach to Domain Adaptation in Semantic Scene Segmentation | ||
| [ICCV2021] | Uncertainty-Aware Pseudo Label Refinery for Domain Adaptive Semantic Segmentation | ||
| [ICCV2021] | Domain Adaptive Semantic Segmentation With Self-Supervised Depth Estimation | ||
| [ICCV2021] | Generalize Then Adapt: Source-Free Domain Adaptive Semantic Segmentation | ||
| [CVPR2021] | DARCNN: Domain Adaptive Region-Based Convolutional Neural Network for Unsupervised Instance Segmentation in Biomedical Images | ||
| [CVPR2021] | DANNet: A One-Stage Domain Adaptation Network for Unsupervised Nighttime Semantic Segmentation | ||
| [CVPR2021] | Prototypical Pseudo Label Denoising and Target Structure Learning for Domain Adaptive Semantic Segmentation | ||
| [CVPR2021] | Complete & Label: A Domain Adaptation Approach to Semantic Segmentation of LiDAR Point Clouds | ||
| [CVPR2021] | Cluster, Split, Fuse, and Update: Meta-Learning for Open Compound Domain Adaptive Semantic Segmentation | ||
| [CVPR2021] | PixMatch: Unsupervised Domain Adaptation via Pixelwise Consistency Training | ||
| [CVPR2021] | Learning to Relate Depth and Semantics for Unsupervised Domain Adaptation | ||
| [CVPR2021] | Cross-View Regularization for Domain Adaptive Panoptic Segmentation | ||
| [CVPR2021] | Semi-supervised Domain Adaptation based on Dual-level Domain Mixing for Semantic Segmentation | ||
| [CVPR2021] | MetaCorrection: Domain-aware Meta Loss Correction for Unsupervised Domain Adaptation in Semantic Segmentation | ||
| [CVPR2021] | Coarse-to-Fine Domain Adaptive Semantic Segmentation with Photometric Alignment and Category-Center Regularization | ||
| [CVPR2021] | Multi-Source Domain Adaptation with Collaborative Learning for Semantic Segmentation | ||
| [CVPR2021] | Source-Free Domain Adaptation for Semantic Segmentation | ||
| [ECCV 2020] | Instance Adaptive Self-Training for Unsupervised Domain Adaptation | ||
| [ICASSP 2020] | Cross-stained Segmentation from Renal Biopsy Images Using Multi-level Adversarial Learning | ||
| [NeurlIPS 2020] | Pixel-Level Cycle Association: A New Perspective for Domain Adaptive Semantic Segmentation | ||
| [NeurIPS2020] | Adversarial Style Mining for One-Shot Unsupervised Domain Adaptation | ||
| [BMVC2020] | Semantically Adaptive Image-to-image Translation for Domain Adaptation of Semantic Segmentation | ||
| [ECCV2020] | Contextual-Relation Consistent Domain Adaptation for Semantic Segmentation | ||
| [ECCV2020] | Learning from Scale-Invariant Examples for Domain Adaptation in Semantic Segmentation | ||
| [ECCV2020] | Label-Driven Reconstruction for Domain Adaptation in Semantic Segmentation | ||
| [ECCV2020] | Unsupervised Domain Adaptation for Semantic Segmentation of NIR Images through Generative Latent Search | ||
| [ECCV2020] | Domain Adaptive Semantic Segmentation Using Weak Labels | ||
| [ECCV2020] | Content-Consistent Matching for Domain Adaptive Semantic Segmentation | ||
| [CVPR2020] | Cross-Domain Semantic Segmentation via Domain-Invariant Interactive Relation Transfer | ||
| [CVPR2020] | Phase Consistent Ecological Domain Adaptation | ||
| [CVPR2020] | FDA: Fourier Domain Adaptation for Semantic Segmentation | ||
| [CVPR2020] | Unsupervised Instance Segmentation in Microscopy Images via Panoptic Domain Adaptation and Task Re-weighting | ||
| [CVPR2020 Oral] | Unsupervised Intra-domain Adaptation for Semantic Segmentation through Self-Supervision | ||
| [CVPR2020] | Differential Treatment for Stuff and Things: A Simple Unsupervised Domain Adaptation Method for Semantic Segmentation | ||
| [CVPR2020] | Learning Texture Invariant Representation for Domain Adaptation of Semantic Segmentation | ||
| [CVPR2020] | xMUDA: Cross-Modal Unsupervised Domain Adaptation for 3D Semantic Segmentation | ||
| [IJCAI2020] | Unsupervised Scene Adaptation with Memory Regularization in vivo | ||
| [AAAI2020] | Joint Adversarial Learning for Domain Adaptation in Semantic Segmentation | ||
| [AAAI2020] | An Adversarial Perturbation Oriented Domain Adaptation Approach for Semantic Segmentation | ||
| [NeurIPS2019] | Category Anchor-Guided Unsupervised Domain Adaptation for Semantic Segmentation | ||
| [WACV2020] | MLSL: Multi-Level Self-Supervised Learning for Domain Adaptation with Spatially Independent and Semantically Consistent Labeling | ||
| [WACV2020] | Domain Bridge for Unpaired Image-to-Image Translation and Unsupervised Domain Adaptation | ||
| [ICCV2019] | Guided Curriculum Model Adaptation and Uncertainty-Aware Evaluation for Semantic Nighttime Image Segmentation | ||
| [ICCV2019] | Constructing Self-motivated Pyramid Curriculums for Cross-Domain Semantic Segmentation: A Non-Adversarial Approach | ||
| [ICCV2019] | SSF-DAN: Separated Semantic Feature Based Domain Adaptation Network for Semantic Segmentation | ||
| [ICCV2019] | Significance-aware Information Bottleneck for Domain Adaptive Semantic Segmentation | ||
| [ICCV2019] | Domain Adaptation for Semantic Segmentation with Maximum Squares Loss | ||
| [ICCV2019] | Self-Ensembling with GAN-based Data Augmentation for Domain Adaptation in Semantic Segmentation | ||
| [ICCV2019] | DADA: Depth-aware Domain Adaptation in Semantic Segmentation | ||
| [ICCV2019 Oral] | Domain Adaptation for Structured Output via Discriminative Patch Representations | ||
| [CVPR2019(Oral)] | Not All Areas Are Equal: Transfer Learning for Semantic Segmentation via Hierarchical Region Selection | ||
| [CVPR2019] | CrDoCo: Pixel-level Domain Transfer with Cross-Domain Consistency | ||
| [CVPR2019] | Bidirectional Learning for Domain Adaptation of Semantic Segmentation | ||
| [CVPR2019] | Learning Semantic Segmentation from Synthetic Data: A Geometrically Guided Input-Output Adaptation Approach | ||
| [CVPR2019] | All about Structure: Adapting Structural Information across Domains for Boosting Semantic Segmentation | ||
| [CVPR2019 Oral] | DLOW: Domain Flow for Adaptation and Generalization | ||
| [CVPR2019 Oral] | Taking A Closer Look at Domain Shift: Category-level Adversaries for Semantics Consistent Domain Adaptation | ||
| [CVPR2019 Oral] | ADVENT: Adversarial Entropy Minimization for Domain Adaptation in Semantic Segmentation | ||
| [ICLR2019] | SPIGAN: Privileged Adversarial Learning from Simulation | ||
| [ECCV2018] | Penalizing Top Performers: Conservative Loss for Semantic Segmentation Adaptation | ||
| [ECCV2018] | Domain transfer through deep activation matching | ||
| [ECCV2018] | Unsupervised Domain Adaptation for Semantic Segmentation via Class-Balanced Self-Training | ||
| [ECCV2018] | DCAN: Dual channel-wise alignment networks for unsupervised scene adaptation | ||
| [CVPR2018] | Fully convolutional adaptation networks for semantic segmentation | ||
| [CVPR2018] | Learning to Adapt Structured Output Space for Semantic Segmentation | ||
| [CVPR2018] | Conditional Generative Adversarial Network for Structured Domain Adaptation | ||
| [CVPR2018] | Learning From Synthetic Data: Addressing Domain Shift for Semantic Segmentation | ||
| [ICCV2017] | Curriculum Domain Adaptation for Semantic Segmentation of Urban Scenes | ||
| [ICCV2017] | No more discrimination: Cross city adaptation of road scene segmenters | ||
| [TPAMI2023] | SePiCo: Semantic-Guided Pixel Contrast for Domain Adaptive Semantic Segmentation | ||
| [TIP2022] | Adaptive Boosting for Domain Adaptation: Towards Robust Predictions in Scene Segmentation | ||
| [IJCV2020] | Rectifying Pseudo Label Learning via Uncertainty Estimation for Domain Adaptive Semantic Segmentation | ||
| [Neurocomputing 2021] | Multi-level colonoscopy malignant tissue detection with adversarial CAC-UNet | ||
| [TIP2020] | Affinity Space Adaptation for Semantic Segmentation Across Domains | ||
| [Neurocomputing 2019] | Semantic-aware short path adversarial training for cross-domain semantic segmentation | ||
| [TIP] | Weakly Supervised Adversarial Domain Adaptation for Semantic Segmentation in Urban Scenes | ||
| [27 Nov 2019] | Class-Conditional Domain Adaptation on Semantic Segmentation | ||
| [2 Sep 2019] | Adversarial Learning and Self-Teaching Techniques for Domain Adaptation in Semantic Segmentation | ||
| [8 Dec 2016] | FCNs in the Wild: Pixel-level Adversarial and Constraint-based Adaptation | ||
| [13 Oct 2020] | BoMuDA: Boundless Multi-Source Domain Adaptive Segmentation in Unconstrained Environments | ||
| [27 Nov 2020] | SAfE: Self-Attention Based Unsupervised Road Safety Classification in Hazardous Environments | ||
| [26 Jun 2021] | Semantics-aware Multi-modal Domain Translation:From LiDAR Point Clouds to Panoramic Color Images | ||
Papers / Applications / Person Re-identification | |||
| [CVPR 2021] | Learning to Generalize Unseen Domains via Memory-based Multi-Source Meta-Learning for Person Re-Identification | ||
| [CVPR2021] | Group-aware Label Transfer for Domain Adaptive Person Re-identification | ||
| [ECCV2020] | Unsupervised Domain Adaptation in the Dissimilarity Space for Person Re-identification | ||
| [ECCV2020] | Joint Visual and Temporal Consistency for Unsupervised Domain Adaptive Person Re-Identification | ||
| [ECV2020] | Joint Disentangling and Adaptation for Cross-Domain Person Re-Identification | ||
| [ECCV2020] | Multiple Expert Brainstorming for Domain Adaptive Person Re-identification | ||
| [ECCV2020] | Deep Credible Metric Learning for Unsupervised Domain Adaptation Person Re-identification | ||
| [ECCV2020] | Unsupervised Domain Adaptation with Noise Resistible Mutual-Training for Person Re-identification | ||
| [ECCV2020] | Generalizing Person Re-Identification by Camera-Aware Invariance Learning and Cross-Domain Mixup | ||
| [CVPR2020] | AD-Cluster: Augmented Discriminative Clustering for Domain Adaptive Person Re-identification | ||
| [CVPR2020] | Smoothing Adversarial Domain Attack and P-Memory Reconsolidation for Cross-Domain Person Re-Identification | ||
| [CVPR2020] | Cross-Modal Cross-Domain Moment Alignment Network for Person Search | ||
| [CVPR2020] | Online Joint Multi-Metric Adaptation From Frequent Sharing-Subset Mining for Person Re-Identification | ||
| [ICLR2020] | Mutual Mean-Teaching: Pseudo Label Refinery for Unsupervised Domain Adaptation on Person Re-identification | ||
| [ICCV2019 Oral] | Self-similarity Grouping: A Simple Unsupervised Cross Domain Adaptation Approach for Person Re-identification | ||
| [ICCV2019] | A Novel Unsupervised Camera-aware Domain Adaptation Framework for Person Re-identification | ||
| [CVPR2019] | Invariance Matters: Exemplar Memory for Domain Adaptive Person Re-identification | ||
| [ECCV2018] | Domain Adaptation through Synthesis for Unsupervised Person Re-identification | ||
| [CVPR2018] | Person Transfer GAN to Bridge Domain Gap for Person Re-Identification | ||
| [CVPR2018] | Image-Image Domain Adaptation with Preserved Self-Similarity and Domain-Dissimilarity for Person Re-identification | ||
| [arXiv 22 Apr 2021] | Domain Adaptation for Semantic Segmentation via Patch-Wise Contrastive Learning | ||
| [arXiv 14 Mar 2020] | Structured Domain Adaptation for Unsupervised Person Re-identification | ||
| [arXiv 25 May 2019] | Domain Adaptive Attention Model for Unsupervised Cross-Domain Person Re-Identification | ||
| [arXiv 2 Apr 2019] | Camera Adversarial Transfer for Unsupervised Person Re-Identification | ||
| [arXiv 29 Dec 2018] | EANet: Enhancing Alignment for Cross-Domain Person Re-identification | ||
| [arXiv 26 Nov 2018] | One Shot Domain Adaptation for Person Re-Identification | ||
| [arXiv 26 Nov 2018] | Similarity-preserving Image-image Domain Adaptation for Person Re-identification | ||
Papers / Applications / Sim-to-Real Transfer | |||
| [CoRL2020] | DIRL: Domain-Invariant Reperesentation Learning Approach for Sim-to-Real Transfer | ||
Papers / Applications / Video Domain Adaptation | |||
| https://dl.acm.org/doi/abs/10.1145/3571600.3571621 | Overcoming Label Noise for Source-free Unsupervised Video Domain Adaptation [[ICVGIP'22]] ( ) [Pytorch]]( ) [[Project]] ( ) [[Extended Version]] ( ) | ||
| [ECCV2022] | Source-free Video Domain Adaptation by Learning Temporal Consistency for Action Recognition | ||
| [NeurIPS2021] | Contrast and Mix: Temporal Contrastive Video Domain Adaptation with Background Mixing | ||
| [ICCV2021] | Learning Cross-Modal Contrastive Features for Video Domain Adaptation | ||
| [ICCV2021] | Partial Video Domain Adaptation With Partial Adversarial Temporal Attentive Network | ||
| [ICCV2021] | Domain Adaptive Video Segmentation via Temporal Consistency Regularization | ||
| [ECCV2020] | Shuffle and Attend: Video Domain Adaptation | ||
| [CVPR2020] | Transferring Cross-Domain Knowledge for Video Sign Language Recognition | ||
| [CVPR2020] | Action Segmentation with Joint Self-Supervised Temporal Domain Adaptation | ||
| [CVPR2020 Oral] | Transferring Cross-domain Knowledge for Video Sign Language Recognition | ||
| [CVPR2020 Oral] | Multi-Modal Domain Adaptation for Fine-Grained Action Recognition | ||
| [AAAI2020] | Adversarial Cross-Domain Action Recognition with Co-Attention | ||
| [Paper] | Generative Adversarial Networks for Video-to-Video Domain Adaptation | ||
| [ICCV2019 Oral] | Temporal Attentive Alignment for Large-Scale Video Domain Adaptation | ||
| [CVPRW 2019] | Temporal Attentive Alignment for Video Domain Adaptation | ||
| [17 Nov 2022] | Video Unsupervised Domain Adaptation with Deep Learning: A Comprehensive Survey | ||
| [15 Aug 2022] | Unsupervised Video Domain Adaptation: A Disentanglement Perspective | ||
| [5 Aug 2019] | Image to Video Domain Adaptation Using Web Supervision | ||
Papers / Applications / Medical Related | |||
| [PSB 2024] | PopGenAdapt: Semi-Supervised Domain Adaptation for Genotype-to-Phenotype Prediction in Underrepresented Populations | ||
| [ICASSP 2020] | Cross-stained Segmentation from Renal Biopsy Images Using Multi-level Adversarial Learning | ||
| [Paper] | What Can Be Transferred: Unsupervised Domain Adaptation for Endoscopic Lesions Segmentation | ||
| [ICCV2019] | Semantic-Transferable Weakly-Supervised Endoscopic Lesions Segmentation | ||
| [Neurocomputing 2021] | Multi-level colonoscopy malignant tissue detection with adversarial CAC-UNet | ||
| [10 Mar 2022] | On-the-Fly Test-time Adaptation for Medical Image Segmentation | ||
| [10 Mar 2023] | Target and task specific source-free domain adaptive image segmentation | ||
| [arXiv 29 Aug 2019] | Unsupervised Domain Adaptation via Disentangled Representations: Application to Cross-Modality Liver Segmentation | ||
| [arXiv on 24 Jan 2019] | Synergistic Image and Feature Adaptation: Towards Cross-Modality Domain Adaptation for Medical Image Segmentation | ||
| [arXiv 14 Nov 2018] | Unsupervised domain adaptation for medical imaging segmentation with self-ensembling | ||
Papers / Applications / Monocular Depth Estimation | |||
| [CVPR2019] | Geometry-Aware Symmetric Domain Adaptation for Monocular Depth Estimation | ||
| [CVPR2018] | Real-Time Monocular Depth Estimation using Synthetic Data with Domain Adaptation via Image Style Transfer | ||
Papers / Applications / 3D | |||
| [ICCV2021] | SPG: Unsupervised Domain Adaptation for 3D Object Detection via Semantic Point Generation | ||
| [ICCV2021] | Sparse-to-Dense Feature Matching: Intra and Inter Domain Cross-Modal Learning in Domain Adaptation for 3D Semantic Segmentation | ||
| [ICCV2021] | Unsupervised Domain Adaptive 3D Detection With Multi-Level Consistency | ||
| [ICCV2019] | Domain-Adaptive Single-View 3D Reconstruction | ||
| [Arxiv 11 Aug 2023] | MS3D++: Ensemble of Experts for Multi-Source Unsupervised Domain Adaptation in 3D Object Detection | ||
Papers / Applications / Fine-Grained Domain | |||
| [CVPR2020] | Progressive Adversarial Networks for Fine-Grained Domain Adaptation | ||
Papers / Applications / LiDAR | |||
| [3DV 2024] | SALUDA: Surface-based Automotive Lidar Unsupervised Domain Adaptation | ||
| [ECCV2022] | GIPSO: Geometrically Informed Propagation for Online Adaptation in 3D LiDAR Segmentation | ||
| [ECCV2022] | CoSMix: Compositional Semantic Mix for Domain Adaptation in 3D LiDAR Segmentation | ||
| [13 Mar 2022] | ConDA: Unsupervised Domain Adaptation for LiDAR Segmentation via Regularized Domain Concatenation | ||
Papers / Applications / Remote Sensing | |||
| [GRSL 2023] | Open-Set Black-Box Domain Adaptation for Remote Sensing Image Scene Classification | ||
Papers / Applications / Others | |||
| [3DV 2021 Oral] | RefRec: Pseudo-labels Refinement via Shape Reconstruction for Unsupervised 3D Domain Adaptation | ||
| [ICRA2022] | Unsupervised Domain Adaptation in LiDAR Semantic Segmentation with Self-Supervision and Gated Adapters | ||
| [ICCV2021] | RDA: Robust Domain Adaptation via Fourier Adversarial Attacking | ||
| [ICCV2021] | Geometry-Aware Self-Training for Unsupervised Domain Adaptation on Object Point Clouds | ||
| [ICCV2021] | Tune It the Right Way: Unsupervised Validation of Domain Adaptation via Soft Neighborhood Density | ||
| [ICCV2021] | PIT: Position-Invariant Transform for Cross-FoV Domain Adaptation | ||
| [ICCV2021] | Self-Supervised Domain Adaptation for Forgery Localization of JPEG Compressed Images | ||
| [ICCV2021] | Unsupervised Real-World Super-Resolution: A Domain Adaptation Perspective | ||
| [ICCV2021] | Adversarial Robustness for Unsupervised Domain Adaptation | ||
| [ICCV2021] | Collaborative Optimization and Aggregation for Decentralized Domain Generalization and Adaptation | ||
| [CVPR2021] | Adaptive Cross-Modal Prototypes for Cross-Domain Visual-Language Retrieval | ||
| [CVPR2021] | Spatio-temporal Contrastive Domain Adaptation for Action Recognition | ||
| [CVPR2021] | Regressive Domain Adaptation for Unsupervised Keypoint Detection | ||
| [CVPR2021] | From Synthetic to Real: Unsupervised Domain Adaptation for Animal Pose Estimation | ||
| [ICCV Workshop 2021] | Meta Self-Learning for Multi-Source Domain Adaptation: A Benchmark | ||
| [NeurlPS 2020] | 7 | over 5 years ago | Adapting Neural Architectures Between Domains |
| [ECCV2020] | Unsupervised Domain Attention Adaptation Network for Caricature Attribute Recognition | ||
| [ECCV2020] | A Broader Study of Cross-Domain Few-Shot Learning | ||
| [ICML2020] | Label-Noise Robust Domain Adaptation | ||
| [IJCAI2020] | Unsupervised Domain Adaptation of a Pretrained Cross-Lingual Language Model | ||
| [IJCAI2020] | Domain Adaptation for Semantic Parsing | ||
| [IJCAI2020] | Bridging Cross-Tasks Gap for Cognitive Assessment via Fine-Grained Domain Adaptation | ||
| [IJCAI2020] | Clarinet: A One-step Approach Towards Budget-friendly Unsupervised Domain Adaptation | ||
| [CVPR2020] | Weakly-Supervised Domain Adaptation via GAN and Mesh Model for Estimating 3D Hand Poses Interacting Objects | ||
| [CVPR2020] | One-Shot Domain Adaptation for Face Generation | ||
| [CVPR2020 Oral] | Learning Meta Face Recognition in Unseen Domains | ||
| [CVPR2020] | Cross-Domain Document Object Detection: Benchmark Suite and Method | ||
| [CVPR2020] | StereoGAN: Bridging Synthetic-to-Real Domain Gap by Joint Optimization of Domain Translation and Stereo Matching | ||
| [CVPR2020] | Domain Adaptation for Image Dehazing | ||
| [CVPR2020] | Probability Weighted Compact Feature for Domain Adaptive Retrieval | ||
| [CVPR2020] | Disparity-Aware Domain Adaptation in Stereo Image Restoration | ||
| [CVPR2020] | Multi-Path Learning for Object Pose Estimation Across Domains | ||
| [ACM MM2019] | Unsupervised Domain Adaptation for 3D Human Pose Estimation | ||
| [NeurIPS 2019] | PointDAN: A Multi-Scale 3D Domain Adaption Network for Point Cloud Representation | ||
| [ICCV2019] | Deep Head Pose Estimation Using Synthetic Images and Partial Adversarial Domain Adaption for Continuous Label Spaces | ||
| [ICCV2019] | Cross-Domain Adaptation for Animal Pose Estimation | ||
| [ICCV2019] | GA-DAN: Geometry-Aware Domain Adaptation Network for Scene Text Detection and Recognition | ||
| [IJCNN] | Accelerating Deep Unsupervised Domain Adaptation with Transfer Channel Pruning | ||
| [WWW2019] | Adversarial Adaptation of Scene Graph Models for Understanding Civic Issues | ||
| [CVPR2018] | Cross-Dataset Adaptation for Visual Question Answering | ||
| [DYCOPS2022] | Cross-domain fault diagnosis through optimal transport for a CSTR process | ||
| [TIP2020] | DASGIL: Domain Adaptation for Semantic and Geometric-Aware Image-Based Localization | ||
| [Image and Vision Computing 2020] | An Unsupervised Domain Adaptation Scheme for Single-Stage Artwork Recognition in Cultural Sites | ||
| [JIntellManuf2020] | Multi-source transfer learning of time series in cyclical manufacturing | ||
| [KBS2020] | Domain adaptation for regression under Beer-Lambert's law | ||
| [11 Nov 2019] | Open-Ended Visual Question Answering by Multi-Modal Domain Adaptation | ||
| [arXiv 3 Jun 2019] | DANE: Domain Adaptive Network Embedding | ||
| [arXiv 16 Apr 2019] | Active Adversarial Domain Adaptation | ||
Papers / Benchmarks | |||
| [PAA2023] | Kurcuma: a kitchen utensil recognition collection for unsupervised domain adaptation | ||
| [ICCV Workshop 2021] | Meta Self-Learning for Multi-Source Domain Adaptation: A Benchmark | ||
| [ICCV Workshop 2021] | LLVIP: A Visible-infrared Paired Dataset for Low-light Vision | ||
| [arXiv 26 Jun] | Syn2Real: A New Benchmark forSynthetic-to-Real Visual Domain Adaptation | ||
| [ICLR 2019] | Benchmarking Neural Network Robustness to Common Corruptions and Perturbations (ImageNet-C) | ||
Library | |||
| Transfer-Learning-Library | 3,484 | over 2 years ago | |
| deep-transfer-learning: a PyTorch library for deep transfer learning | 898 | over 4 years ago | |
| salad: a Semi-supervised Adaptive Learning Across Domains | |||
| Dassl: a PyTorch toolbox for domain adaptation and semi-supervised learning | 1,236 | almost 3 years ago | |
| joliGEN: an integrated framework for training custom generative AI image-to-image models | 246 | almost 2 years ago | |
Lectures and Tutorials | |||
| [PDF] | A Primer on Domain Adaptation | ||
Other Resources | |||
| transferlearning | 13,547 | almost 2 years ago | |
jbhuang0604/awesome-computer-vision
mlgroupjlu/llm-eval-survey
jolibrain/deepdetect