- modified: 2023-12-16
- PyOD (Python Outlier Detection) : URL | Document
- TDOS (Time-series Outlier Detection Systems) : URL | Document
- PyGOD (Python Graph Outlier Detection) : URL | Document
- alibi-detect : URL | Document
- PyNomaly: URL
- DeepOD: URL
- SL-GAD: Generative and contrastive self-supervised learning for graph anomaly detection (TKDE'21)
- ANEMONE: Anemone: Graph anomaly detection with multi-scale contrastive learning (CIKM'21)
- CoLA: Anomaly detection on attributed networks via contrastive self-supervised learning (TNNLS'21)
- PC-GNN: Pick and Choose: A GNN-based Imbalanced Learning Approach for Fraud Detection (WWW'21)
- CARE-GNN: Enhancing Graph Neural Network-based Fraud Detectors against Camouflaged Fraudsters (CIKM'20)
- OCGNN: One-class graph neural networks for anomaly detection in attributed networks (NCA'20)
- AnomalyDAE: AnomalyDAE: Dual autoencoder for anomaly detection on attributed networks (ICASSP'20)
- SpecAE: SpecAE: Spectral autoencoder for anomaly detection in attributed networks (CIKM'19)
- DOMINANT: Deep anomaly detection on attributed networks (SDM'19)
- ANOMALOUS:Anomalous: A joint modeling approach for anomaly detection on attributed networks (IJCAI'18)
- OddBall: OddBall: Spotting anomalies in weighted graphs (PAKDD'10)
- MVTec AD:
Industrial inspection - MVTec LOCO AD:
MVTec Logical Constraints Anomaly Detection - VisA:
Visual Anomaly Dataset - Kolektor:
Surface defect - ADNI:
Alzheimer’s Disease Neuroimaging Initiative - Decathlon:
Medical Segmentation Decathlon - ELPV:
High-resolution Electroluminescence - CRDDC:
Crowdsensing-based Road Damage Detection Challenge - ABU:
Airport-Beach-Urban (ABU)
- MemSeg: A semi-supervised method for image surface defect detection using differences and commonalities (EAAI'23)
- Multi-Scale Patch-Based Representation Learning for Image Anomaly Detection and Segmentation (WACV'22)
- Anomaly Detection via Reverse Distillation from One-Class Embedding (CVPR'22)
- Multiresolution knowledge distillation for anomaly detection (CVPR'21)
- PaDiM: a Patch Distribution Modeling Framework for Anomaly Detection and Localization (ICPR'21)
- DRAEM-A discriminatively trained reconstruction embedding for surface anomaly detection (ICCV'21)
- Uninformed Students: Student–Teacher Anomaly Detection with Discriminative Latent Embeddings (CVPR'20)
- Patch SVDD: Patch-level SVDD for Anomaly Detection and Segmentation (ACCV'20)
- Modeling the Distribution of Normal Data in Pre-Trained Deep Features for Anomaly Detection (ICPR'20)
- Sub-Image Anomaly Detection with Deep Pyramid Correspondences (arXiv'20)
- Memorizing Normality to Detect Anomaly: Memory-Augmented Deep Autoencoder for Unsupervised Anomaly Detection (ICCV'19)
- Adversarially learned one-class classifier for novelty detection (CVPR'18)
- Unsupervised Anomaly Detection with Generative Adversarial Networks to Guide Marker Discovery (IPMI'17)
- Deep One-Class Classification (ICML'18)
- Auto-Encoding Variational Bayes (ICLR'14)
- Anomaly Transformer: Time Series Anomaly Detection with Association Discrepancy (ICLR'22)
- Inpainting Transformer for Anomaly Detection (ICIAP'22)
- AnoViT: Unsupervised anomaly detection and localization with vision transformer-based encoder-decoder (IEEE Acess'22)
- Multivariate time se- ries anomaly detection and interpretation using hierarchical inter-metric and temporal embedding (KDD'21)
- VT-ADL: A vision transformer network for image anomaly detection and localization (ISIE'21)
- USAD: UnSupervised Anomaly Detection on Multivariate Time Series (KDD'20)
- Timeseries anomaly detection using temporal hierarchical one-class network (NIPS'20)
- Integrative tensor-based anomaly detection system for reducing false positives of satellite systems (CIKM'20)
- Detecting anomalies in space using multivariate convolutional lstm with mixtures of probabilistic pca (KDD'19)
- Robust anomaly detection for multivariate time series through stochastic recurrent neural network (KDD'19)
- Outlier Detection for Time Series with Recurrent Autoencoder Ensembles (IJCAI'19)
- Time-Series Anomaly Detection Service at Microsoft (KDD'19)
- Deep autoencoding gaussian mixture model for unsupervised anomaly detection (ICLR'18)
- A Multimodal Anomaly Detector for Robot-Assisted Feeding Using an LSTM-Based Variational Autoencoder (RA-L'18)
- LSTM-based Encoder-Decoder for Multi-sensor Anomaly Detection (ICML'16)
- Convolutional transformer based dual discriminator general adversarial networks for video anomaly detection (MM'21)
- A hybrid video anomaly detection framework via memory-augmented flow reconstruction and flow-guided frame prediction (ICCV'21)
- Cloze test helps: Effective video anomaly detection via learning to complete video events (MM'20)
- Clustering driven deep autoencoder for video anomaly detection (ECCV'20)
- Learning memory-guided normality for anomaly detection (CVPR'20)
- Video anomaly detection and localization via gaussian mixture fully convolutional variational autoencoder (CVIU'20)
- Learning regularity in skeleton trajectories for anomaly detection in videos (CVPR'19)
- Memorizing normality to detect anomaly: Memory-augmented deep autoencoder for unsupervised anomaly detection (ICCV'19)
- Anomaly detection in video sequence with appearancemotion correspondence (ICCV'19)
- Anopcn: Video anomaly detection via deep predictive coding network (MM'19)
- Future frame prediction using convolutional vrnn for anomaly detection (AVSS'19)
- Bman: bidirectional multi-scale aggregation networks for abnormal event detection (TIP'19)
- Future frame prediction for anomaly detection a new baseline (CVPR'18)
- Real-world Anomaly Detection in Surveillance Videos (CVPR'18)
- Remembering history with convolutional lstm for anomaly detection (ICME'17)
- Spatio-temporal autoencoder for video anomaly detection (MM'17)
- Learning temporal regularity in video sequences (CVPR'16)
- Abnormal event detection in crowded scenes using sparse representation (PR'13)
- Video parsing for abnormality detection (ICCV'11)
- Robust real-time unusual event detection using multiple fixed-location monitors (TRAMI'08)
- A Survey on Unsupervised Visual Industrial Anomaly Detection Algorithms (2022.08)
- A Survey on Graph Neural Networks and Graph Transformers in Computer Vision: A Task-Oriented Perspective (CoRR'22)
- A Comprehensive Survey on Graph Anomaly Detection with Deep Learning (TKDE'21)
- Deep Learning for Anomaly Detection: A Survey (2019.01)
- Anomaly Detection: A Survey (2009.06)