awesome-anomaly-detection
Data outlier detection catalog
A curated list of resources on detecting unusual patterns in data
A curated list of awesome anomaly detection resources
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anomalyanomaly-detectionanomalydetectionawesomeawesome-anomaly-detectionawesomeanomalydetectiondeep-learningmachine-learningmachinelearning
awesome anomaly detection / Survey Paper | |||
| [pdf] | Deep Learning for Anomaly Detection: A Survey | | | ||
| [pdf] | Anomalous Instance Detection in Deep Learning: A Survey | | | ||
| [pdf] | Deep Learning for Anomaly Detection: A Review | | | ||
| [pdf] | A Unifying Review of Deep and Shallow Anomaly Detection | | | ||
| [pdf] | A Unified Survey on Anomaly, Novelty, Open-Set, and Out-of-Distribution Detection: Solutions and Future Challenges | | | ||
awesome anomaly detection / Time-series anomaly detection (need to survey more..) | |||
| [pdf] | Anomaly Detection of Time Series | | | ||
| [pdf] | Long short term memory networks for anomaly detection in time series | | | ||
| [pdf] | LSTM-Based System-Call Language Modeling and Robust Ensemble Method for Designing Host-Based Intrusion Detection Systems | | | ||
| [pdf] | Time Series Anomaly Detection; Detection of anomalous drops with limited features and sparse examples in noisy highly periodic data | | | ||
| [pdf] | Anomaly Detection in Multivariate Non-stationary Time Series for Automatic DBMS Diagnosis | | | ||
| [pdf] | Truth Will Out: Departure-Based Process-Level Detection of Stealthy Attacks on Control Systems | | | ||
| [pdf] | DeepAnT: A Deep Learning Approach for Unsupervised Anomaly Detection in Time Series | | | ||
| [pdf] | Time-Series Anomaly Detection Service at Microsoft | | | ||
| [pdf] | Robust Anomaly Detection for Multivariate Time Series through Stochastic Recurrent Neural Network | | | ||
| [code] | 566 | over 2 years ago | A Systematic Evaluation of Deep Anomaly Detection Methods for Time Series | | |
| [pdf] | BeatGAN: Anomalous Rhythm Detection using Adversarially Generated Time | | | ||
| [pdf] | MIDAS: Microcluster-Based Detector of Anomalies in Edge Streams | | | | ||
awesome anomaly detection / Video-level anomaly detection | |||
| [pdf] | Abnormal Event Detection in Videos using Spatiotemporal Autoencoder | | | ||
| [pdf] | Real-world Anomaly Detection in Surveillance Videos | | | ||
| [pdf] | Unsupervised Anomaly Detection for Traffic Surveillance Based on Background Modeling | | | ||
| [pdf] | Dual-Mode Vehicle Motion Pattern Learning for High Performance Road Traffic Anomaly Detection | | | ||
| [link] | Detecting Abnormality without Knowing Normality: A Two-stage Approach for Unsupervised Video Abnormal Event Detection | | | ||
| [pdf] | Motion-Aware Feature for Improved Video Anomaly Detection | | | ||
| [pdf] | Challenges in Time-Stamp Aware Anomaly Detection in Traffic Videos | | | ||
| [pdf] | Learning Regularity in Skeleton Trajectories for Anomaly Detection in Videos | | | ||
| [pdf] | Graph Convolutional Label Noise Cleaner: Train a Plug-and-play Action Classifier for Anomaly Detection | [CVPR'19] | | ||
| [pdf] | Graph Embedded Pose Clustering for Anomaly Detection | | | ||
| [pdf] | Self-Trained Deep Ordinal Regression for End-to-End Video Anomaly Detection | | | ||
| [pdf] | Learning Memory-Guided Normality for Anomaly Detection | | | ||
| [pdf] | Clustering-driven Deep Autoencoder for Video Anomaly Detection | | | ||
| [pdf] | CLAWS: Clustering Assisted Weakly Supervised Learning with Normalcy Suppression for Anomalous Event Detection | | | ||
| [pdf] | Cloze Test Helps: Effective Video Anomaly Detection via Learning to Complete Video Events | | | | ||
| [pdf] | A Self-Reasoning Framework for Anomaly Detection Using Video-Level Labels | | | ||
| [pdf] | Re Learning Memory Guided Normality for Anomaly Detection | | | ||
| [pdf] | Weakly-supervised Video Anomaly Detection with Robust Temporal Feature Magnitude Learning | | | | ||
awesome anomaly detection / Image-level anomaly detection / One Class (Anomaly) Classification target | |||
| [pdf] | Estimating the Support of a High- Dimensional Distribution [ ] | | | ||
| [pdf] | A Survey of Recent Trends in One Class Classification | | | ||
| [link] | Anomaly detection using autoencoders with nonlinear dimensionality reduction | | | ||
| [link] | A review of novelty detection | | | ||
| [pdf] | Variational Autoencoder based Anomaly Detection using Reconstruction Probability | | | ||
| [link] | High-dimensional and large-scale anomaly detection using a linear one-class SVM with deep learning | | | ||
| [pdf] | Transfer Representation-Learning for Anomaly Detection | | | ||
| [pdf] | Outlier Detection with Autoencoder Ensembles | | | ||
| [pdf] | Provable self-representation based outlier detection in a union of subspaces | | | ||
| [pdf] | [ ]Adversarially Learned One-Class Classifier for Novelty Detection | | | ||
| [pdf] | Learning Deep Features for One-Class Classification | | | ||
| [pdf] | Efficient GAN-Based Anomaly Detection | | | ||
| [pdf] | Hierarchical Novelty Detection for Visual Object Recognition | | | ||
| [pdf] | Deep One-Class Classification | | | ||
| [pdf] | Reliably Decoding Autoencoders’ Latent Spaces for One-Class Learning Image Inspection Scenarios | | | ||
| [pdf] | q-Space Novelty Detection with Variational Autoencoders | | | ||
| [pdf] | GANomaly: Semi-Supervised Anomaly Detection via Adversarial Training | | | ||
| [pdf] | Deep Anomaly Detection Using Geometric Transformations | | | ||
| [pdf] | Generative Probabilistic Novelty Detection with Adversarial Autoencoders | | | ||
| [pdf] | A loss framework for calibrated anomaly detection | | | ||
| [pdf] | A Practical Algorithm for Distributed Clustering and Outlier Detection | | | ||
| [pdf] | Efficient Anomaly Detection via Matrix Sketching | | | ||
| [pdf] | Adversarially Learned Anomaly Detection | | | ||
| [pdf] | Anomaly Detection With Multiple-Hypotheses Predictions | | | ||
| [pdf] | Exploring Deep Anomaly Detection Methods Based on Capsule Net | | | ||
| [pdf] | Latent Space Autoregression for Novelty Detection | | | ||
| [pdf] | OCGAN: One-Class Novelty Detection Using GANs With Constrained Latent Representations | | | ||
| [pdf] | Unsupervised Learning of Anomaly Detection from Contaminated Image Data using Simultaneous Encoder Training | | | ||
| [pdf] | Using Self-Supervised Learning Can Improve Model Robustness and Uncertainty | | | ||
| [pdf] | Effective End-to-end Unsupervised Outlier Detection via Inlier Priority of Discriminative Network | | | ||
| [pdf] | Classification-Based Anomaly Detection for General Data | | | ||
| [pdf] | Robust Subspace Recovery Layer for Unsupervised Anomaly Detection | | | ||
| [pdf] | RaPP: Novelty Detection with Reconstruction along Projection Pathway | | | ||
| [pdf] | Novelty Detection Via Blurring | | | ||
| [pdf] | Deep Semi-Supervised Anomaly Detection | | | ||
| [pdf] | Robust anomaly detection and backdoor attack detection via differential privacy | | | ||
| [pdf] | Classification-Based Anomaly Detection for General Data | | | ||
| [pdf] | Old is Gold: Redefining the Adversarially Learned One-Class Classifier Training Paradigm | | | ||
| [pdf] | Deep End-to-End One-Class Classifier | | | ||
| [pdf] | Mirrored Autoencoders with Simplex Interpolation for Unsupervised Anomaly Detection | | | ||
| [pdf] | CSI: Novelty Detection via Contrastive Learning on Distributionally Shifted Instances | | | | ||
| [pdf] | Deep Unsupervised Image Anomaly Detection: An Information Theoretic Framework | | | ||
| [pdf] | Regularizing Attention Networks for Anomaly Detection in Visual Question Answering | | | ||
| [pdf] | Attribute Restoration Framework for Anomaly Detection | | | ||
| [pdf] | Modeling the distribution of normal data in pre-trained deep features for anomaly detection | | | | ||
| [pdf] | Discriminative Multi-level Reconstruction under Compact Latent Space for One-Class Novelty Detection | | | ||
| [pdf] | Deep One-Class Classification via Interpolated Gaussian Descriptor | | | | ||
| [pdf] | Multiresolution Knowledge Distillation for Anomaly Detection | | | | ||
| [pdf] | Elsa: Energy-based learning for semi-supervised anomaly detection | | | | ||
awesome anomaly detection / Image-level anomaly detection / Out-of-Distribution(OOD) Detection target | |||
| [pdf] | A Baseline for Detecting Misclassified and Out-of-Distribution Examples in Neural Networks | | | ||
| [pdf] | [ ] Enhancing The Reliability of Out-of-distribution Image Detection in Neural Networks | | | ||
| [pdf] | Training Confidence-calibrated Classifiers for Detecting Out-of-Distribution Samples | | | ||
| [pdf] | Learning Confidence for Out-of-Distribution Detection in Neural Networks | | | ||
| [pdf] | Out-of-Distribution Detection using Multiple Semantic Label Representations | | | ||
| [pdf] | A Simple Unified Framework for Detecting Out-of-Distribution Samples and Adversarial Attacks | | | ||
| [pdf] | Metric Learning for Novelty and Anomaly Detection | | | ||
| [pdf] | Deep Anomaly Detection with Outlier Exposure | | | ||
| [pdf] | Why ReLU networks yield high-confidence predictions far away from the training data and how to mitigate the problem | | | ||
| [pdf] | Outlier Exposure with Confidence Control for Out-of-Distribution Detection | | | ||
| [pdf] | Likelihood Ratios for Out-of-Distribution Detection | | | ||
| [pdf] | Outlier Detection in Contingency Tables Using Decomposable Graphical Models | | | ||
| [pdf] | Input Complexity and Out-of-distribution Detection with Likelihood-based Generative Models | | | ||
| [pdf] | Soft Labeling Affects Out-of-Distribution Detection of Deep Neural Networks | | | ||
| [pdf] | Generalized ODIN: Detecting Out-of-Distribution Image Without Learning From Out-of-Distribution Data | | | ||
| [pdf] | A Boundary Based Out-Of-Distribution Classifier for Generalized Zero-Shot Learning | | | ||
| [pdf] | Provable Worst Case Guarantees for the Detection of Out-of-distribution Data | | | | ||
| [pdf] | On the Value of Out-of-Distribution Testing: An Example of Goodhart's Law | | | ||
| [pdf] | Likelihood Regret: An Out-of-Distribution Detection Score For Variational Auto-encoder | | | ||
| [pdf] | Energy-based Out-of-distribution Detection | | | ||
| [pdf] | Why Normalizing Flows Fail to Detect Out-of-Distribution Data | | | | ||
| [pdf] | Understanding Anomaly Detection with Deep Invertible Networks through Hierarchies of Distributions and Features | | | ||
| [pdf] | CSI: Novelty Detection via Contrastive Learning on Distributionally Shifted Instances | | | | ||
| [pdf] | SSD: A Unified Framework for Self-Supervised Outlier Detection | | | ||
awesome anomaly detection / Image-level anomaly detection / Unsupervised Anomaly Segmentation target | |||
| [pdf] | Anomaly Detection and Localization in Crowded Scenes | | | ||
| [link] | Novelty detection in images by sparse representations | | | ||
| [pdf] | Detecting anomalous structures by convolutional sparse models | | | ||
| [pdf] | Real-Time Anomaly Detection and Localization in Crowded Scenes | | | ||
| [pdf] | Learning Deep Representations of Appearance and Motion for Anomalous Event Detection | | | ||
| [link] | Scale-invariant anomaly detection with multiscale group-sparse models | | | ||
| [pdf] | [ ] Unsupervised Anomaly Detection with Generative Adversarial Networks to Guide Marker Discovery | | | ||
| [pdf] | Deep-Anomaly: Fully Convolutional Neural Network for Fast Anomaly Detection in Crowded Scenes | | | ||
| [pdf] | Anomaly Detection using a Convolutional Winner-Take-All Autoencoder | | | ||
| [pdf] | Anomaly Detection in Nanofibrous Materials by CNN-Based Self-Similarity | | | ||
| [pdf] | Defect Detection in SEM Images of Nanofibrous Materials | | | ||
| [link] | Abnormal event detection in videos using generative adversarial nets | | | ||
| [pdf] | An overview of deep learning based methods for unsupervised and semi-supervised anomaly detection in videos | | | ||
| [pdf] | Improving Unsupervised Defect Segmentation by Applying Structural Similarity to Autoencoders | | | ||
| [pdf] | Satellite Image Forgery Detection and Localization Using GAN and One-Class Classifier | | | ||
| [pdf] | Deep Autoencoding Models for Unsupervised Anomaly Segmentation in Brain MR Images | | | ||
| [pdf] | AVID: Adversarial Visual Irregularity Detection | | | ||
| [pdf] | MVTec AD -- A Comprehensive Real-World Dataset for Unsupervised Anomaly Detection | | | ||
| [pdf] | Exploiting Epistemic Uncertainty of Anatomy Segmentation for Anomaly Detection in Retinal OCT | | | ||
| [pdf] | Uninformed Students: Student-Teacher Anomaly Detection with Discriminative Latent Embeddings | | | ||
| [pdf] | Attention Guided Anomaly Detection and Localization in Images | | | ||
| [pdf] | Sub-Image Anomaly Detection with Deep Pyramid Correspondences | | | | ||
| [pdf] | Patch SVDD, Patch-level SVDD for Anomaly Detection and Segmentation | | | | ||
| [pdf] | Unsupervised anomaly segmentation via deep feature reconstruction | | | | ||
| [pdf] | PaDiM: a Patch Distribution Modeling Framework for Anomaly Detection and Localization | | | | ||
| [pdf] | Explainable Deep One-Class Classification | | | | ||
| [pdf] | Semi-orthogonal Embedding for Efficient Unsupervised Anomaly Segmentation | | ||
| [pdf] | Constrained Contrastive Distribution Learning for Unsupervised Anomaly Detection and Localisation in Medical Images | | | | ||
| [pdf] | Multiresolution Knowledge Distillation for Anomaly Detection | | | ||
awesome anomaly detection / Contact & Feedback | |||
| blog | |||
| pull request | 2,743 | about 2 years ago | |
jbhuang0604/awesome-computer-vision