awesome-anomaly-detection

Deep Learning for Anomaly Detection: A Survey | |

Anomalous Instance Detection in Deep Learning: A Survey | |

Deep Learning for Anomaly Detection: A Review | |

A Unifying Review of Deep and Shallow Anomaly Detection | |

A Unified Survey on Anomaly, Novelty, Open-Set, and Out-of-Distribution Detection: Solutions and Future Challenges | |

Anomaly Detection of Time Series | |

Long short term memory networks for anomaly detection in time series | |

LSTM-Based System-Call Language Modeling and Robust Ensemble Method for Designing Host-Based Intrusion Detection Systems | |

Time Series Anomaly Detection; Detection of anomalous drops with limited features and sparse examples in noisy highly periodic data | |

Anomaly Detection in Multivariate Non-stationary Time Series for Automatic DBMS Diagnosis | |

Truth Will Out: Departure-Based Process-Level Detection of Stealthy Attacks on Control Systems | |

DeepAnT: A Deep Learning Approach for Unsupervised Anomaly Detection in Time Series | |

Time-Series Anomaly Detection Service at Microsoft | |

Robust Anomaly Detection for Multivariate Time Series through Stochastic Recurrent Neural Network | |

[code]

A Systematic Evaluation of Deep Anomaly Detection Methods for Time Series | |

BeatGAN: Anomalous Rhythm Detection using Adversarially Generated Time | |

MIDAS: Microcluster-Based Detector of Anomalies in Edge Streams | | |

Abnormal Event Detection in Videos using Spatiotemporal Autoencoder | |

Real-world Anomaly Detection in Surveillance Videos | |

Unsupervised Anomaly Detection for Traffic Surveillance Based on Background Modeling | |

Dual-Mode Vehicle Motion Pattern Learning for High Performance Road Traffic Anomaly Detection | |

Detecting Abnormality without Knowing Normality: A Two-stage Approach for Unsupervised Video Abnormal Event Detection | |

Motion-Aware Feature for Improved Video Anomaly Detection | |

Challenges in Time-Stamp Aware Anomaly Detection in Traffic Videos | |

Learning Regularity in Skeleton Trajectories for Anomaly Detection in Videos | |

Graph Convolutional Label Noise Cleaner: Train a Plug-and-play Action Classifier for Anomaly Detection | [CVPR'19] |

Graph Embedded Pose Clustering for Anomaly Detection | |

Self-Trained Deep Ordinal Regression for End-to-End Video Anomaly Detection | |

Learning Memory-Guided Normality for Anomaly Detection | |

Clustering-driven Deep Autoencoder for Video Anomaly Detection | |

CLAWS: Clustering Assisted Weakly Supervised Learning with Normalcy Suppression for Anomalous Event Detection | |

Cloze Test Helps: Effective Video Anomaly Detection via Learning to Complete Video Events | | |

A Self-Reasoning Framework for Anomaly Detection Using Video-Level Labels | |

Re Learning Memory Guided Normality for Anomaly Detection | |

Weakly-supervised Video Anomaly Detection with Robust Temporal Feature Magnitude Learning | | |

Estimating the Support of a High- Dimensional Distribution [ ] | |

A Survey of Recent Trends in One Class Classification | |

Anomaly detection using autoencoders with nonlinear dimensionality reduction | |

A review of novelty detection | |

Variational Autoencoder based Anomaly Detection using Reconstruction Probability | |

High-dimensional and large-scale anomaly detection using a linear one-class SVM with deep learning | |

Transfer Representation-Learning for Anomaly Detection | |

Outlier Detection with Autoencoder Ensembles | |

Provable self-representation based outlier detection in a union of subspaces | |

[ ]Adversarially Learned One-Class Classifier for Novelty Detection | |

Learning Deep Features for One-Class Classification | |

Efficient GAN-Based Anomaly Detection | |

Hierarchical Novelty Detection for Visual Object Recognition | |

Deep One-Class Classification | |

Reliably Decoding Autoencoders’ Latent Spaces for One-Class Learning Image Inspection Scenarios | |

q-Space Novelty Detection with Variational Autoencoders | |

GANomaly: Semi-Supervised Anomaly Detection via Adversarial Training | |

Deep Anomaly Detection Using Geometric Transformations | |

Generative Probabilistic Novelty Detection with Adversarial Autoencoders | |

A loss framework for calibrated anomaly detection | |

A Practical Algorithm for Distributed Clustering and Outlier Detection | |

Efficient Anomaly Detection via Matrix Sketching | |

Adversarially Learned Anomaly Detection | |

Anomaly Detection With Multiple-Hypotheses Predictions | |

Exploring Deep Anomaly Detection Methods Based on Capsule Net | |

Latent Space Autoregression for Novelty Detection | |

OCGAN: One-Class Novelty Detection Using GANs With Constrained Latent Representations | |

Unsupervised Learning of Anomaly Detection from Contaminated Image Data using Simultaneous Encoder Training | |

Using Self-Supervised Learning Can Improve Model Robustness and Uncertainty | |

Effective End-to-end Unsupervised Outlier Detection via Inlier Priority of Discriminative Network | |

Classification-Based Anomaly Detection for General Data | |

Robust Subspace Recovery Layer for Unsupervised Anomaly Detection | |

RaPP: Novelty Detection with Reconstruction along Projection Pathway | |

Novelty Detection Via Blurring | |

Deep Semi-Supervised Anomaly Detection | |

Robust anomaly detection and backdoor attack detection via differential privacy | |

Classification-Based Anomaly Detection for General Data | |

Old is Gold: Redefining the Adversarially Learned One-Class Classifier Training Paradigm | |

Deep End-to-End One-Class Classifier | |

Mirrored Autoencoders with Simplex Interpolation for Unsupervised Anomaly Detection | |

CSI: Novelty Detection via Contrastive Learning on Distributionally Shifted Instances | | |

Deep Unsupervised Image Anomaly Detection: An Information Theoretic Framework | |

Regularizing Attention Networks for Anomaly Detection in Visual Question Answering | |

Attribute Restoration Framework for Anomaly Detection | |

Modeling the distribution of normal data in pre-trained deep features for anomaly detection | | |

Discriminative Multi-level Reconstruction under Compact Latent Space for One-Class Novelty Detection | |

Deep One-Class Classification via Interpolated Gaussian Descriptor | | |

Multiresolution Knowledge Distillation for Anomaly Detection | | |

Elsa: Energy-based learning for semi-supervised anomaly detection | | |

A Baseline for Detecting Misclassified and Out-of-Distribution Examples in Neural Networks | |

[ ] Enhancing The Reliability of Out-of-distribution Image Detection in Neural Networks | |

Training Confidence-calibrated Classifiers for Detecting Out-of-Distribution Samples | |

Learning Confidence for Out-of-Distribution Detection in Neural Networks | |

Out-of-Distribution Detection using Multiple Semantic Label Representations | |

A Simple Unified Framework for Detecting Out-of-Distribution Samples and Adversarial Attacks | |

Metric Learning for Novelty and Anomaly Detection | |

Deep Anomaly Detection with Outlier Exposure | |

Why ReLU networks yield high-confidence predictions far away from the training data and how to mitigate the problem | |

Outlier Exposure with Confidence Control for Out-of-Distribution Detection | |

Likelihood Ratios for Out-of-Distribution Detection | |

Outlier Detection in Contingency Tables Using Decomposable Graphical Models | |

Input Complexity and Out-of-distribution Detection with Likelihood-based Generative Models | |

Soft Labeling Affects Out-of-Distribution Detection of Deep Neural Networks | |

Generalized ODIN: Detecting Out-of-Distribution Image Without Learning From Out-of-Distribution Data | |

A Boundary Based Out-Of-Distribution Classifier for Generalized Zero-Shot Learning | |

Provable Worst Case Guarantees for the Detection of Out-of-distribution Data | | |

On the Value of Out-of-Distribution Testing: An Example of Goodhart's Law | |

Likelihood Regret: An Out-of-Distribution Detection Score For Variational Auto-encoder | |

Energy-based Out-of-distribution Detection | |

Why Normalizing Flows Fail to Detect Out-of-Distribution Data | | |

Understanding Anomaly Detection with Deep Invertible Networks through Hierarchies of Distributions and Features | |

CSI: Novelty Detection via Contrastive Learning on Distributionally Shifted Instances | | |

SSD: A Unified Framework for Self-Supervised Outlier Detection | |

Anomaly Detection and Localization in Crowded Scenes | |

Novelty detection in images by sparse representations | |

Detecting anomalous structures by convolutional sparse models | |

Real-Time Anomaly Detection and Localization in Crowded Scenes | |

Learning Deep Representations of Appearance and Motion for Anomalous Event Detection | |

Scale-invariant anomaly detection with multiscale group-sparse models | |

[ ] Unsupervised Anomaly Detection with Generative Adversarial Networks to Guide Marker Discovery | |

Deep-Anomaly: Fully Convolutional Neural Network for Fast Anomaly Detection in Crowded Scenes | |

Anomaly Detection using a Convolutional Winner-Take-All Autoencoder | |

Anomaly Detection in Nanofibrous Materials by CNN-Based Self-Similarity | |

Defect Detection in SEM Images of Nanofibrous Materials | |

Abnormal event detection in videos using generative adversarial nets | |

An overview of deep learning based methods for unsupervised and semi-supervised anomaly detection in videos | |

Improving Unsupervised Defect Segmentation by Applying Structural Similarity to Autoencoders | |

Satellite Image Forgery Detection and Localization Using GAN and One-Class Classifier | |

Deep Autoencoding Models for Unsupervised Anomaly Segmentation in Brain MR Images | |

AVID: Adversarial Visual Irregularity Detection | |

MVTec AD -- A Comprehensive Real-World Dataset for Unsupervised Anomaly Detection | |

Exploiting Epistemic Uncertainty of Anatomy Segmentation for Anomaly Detection in Retinal OCT | |

Uninformed Students: Student-Teacher Anomaly Detection with Discriminative Latent Embeddings | |

Attention Guided Anomaly Detection and Localization in Images | |

Sub-Image Anomaly Detection with Deep Pyramid Correspondences | | |

Patch SVDD, Patch-level SVDD for Anomaly Detection and Segmentation | | |

Unsupervised anomaly segmentation via deep feature reconstruction | | |

PaDiM: a Patch Distribution Modeling Framework for Anomaly Detection and Localization | | |

Explainable Deep One-Class Classification | | |

Semi-orthogonal Embedding for Efficient Unsupervised Anomaly Segmentation |

Constrained Contrastive Distribution Learning for Unsupervised Anomaly Detection and Localisation in Medical Images | | |

Multiresolution Knowledge Distillation for Anomaly Detection | |