Anomaly Detection

Anomaly Detection is a binary classification identifying unusual or unexpected patterns in a dataset, which deviate significantly from the majority of the data. The goal of anomaly detection is to identify such anomalies, which could represent errors, fraud, or other types of unusual events, and flag them for further investigation.

[Image source]: GAN-based Anomaly Detection in Imbalance Problems

Papers

Recently Added Most Hyped Most Active Needs Verification Most Verified

Showing 801–810 of 4856 papers

Title	Date	Tasks	Status	Hype	Score
Iterative weak/self-supervised classification framework for abnormal events detection	Jan 3, 2021	Abnormal Event Detection In VideoAnomaly Detection	CodeCode Available	1	5
A Revealing Large-Scale Evaluation of Unsupervised Anomaly Detection Algorithms	Apr 21, 2022	Anomaly DetectionFraud Detection	CodeCode Available	1	5
Informative knowledge distillation for image anomaly segmentation	Jul 19, 2022	Anomaly DetectionAnomaly Segmentation	CodeCode Available	1	5
A Body Part Embedding Model With Datasets for Measuring 2D Human Motion Similarity	Mar 2, 2021	Action AnalysisAction Recognition	CodeCode Available	1	5
Informative Path Planning for Extreme Anomaly Detection in Environment Exploration and Monitoring	May 20, 2020	Anomaly DetectionBayesian Optimization	CodeCode Available	1	5
Exploring Pose-Based Anomaly Detection for Retail Security: A Real-World Shoplifting Dataset and Benchmark	Jan 11, 2025	Anomaly DetectionPose-based Anomaly Detection	CodeCode Available	1	5
Self-Supervised Predictive Convolutional Attentive Block for Anomaly Detection	Nov 17, 2021	Anomaly DetectionOne-Class Classification	CodeCode Available	1	5
Self-supervised Sparse Representation for Video Anomaly Detection	Oct 23, 2022	Anomaly DetectionAnomaly Detection In Surveillance Videos	CodeCode Available	1	5
A Review on Self-Supervised Learning for Time Series Anomaly Detection: Recent Advances and Open Challenges	Jan 25, 2025	Anomaly DetectionDiversity	CodeCode Available	1	5
A Hierarchical Transformation-Discriminating Generative Model for Few Shot Anomaly Detection	Apr 29, 2021	Anomaly DetectionDefect Detection	CodeCode Available	1	5

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All datasets MVTec AD VisA MVTec LOCO AD One-class CIFAR-10 CUHK Avenue ShanghaiTech Fishyscapes L&F UCR Anomaly Archive MPDD BTAD One-class CIFAR-100 UBnormal

Benchmark Results

#	Model	Metric	Claimed	Verified	Status
1	CPR-faster(TensorRT)	FPS	1,016	—	Unverified
2	CPR-fast(TensorRT)	FPS	362	—	Unverified
3	CPR(TensorRT)	FPS	130	—	Unverified
4	GLASS	Detection AUROC	99.9	—	Unverified
5	UniNet	Detection AUROC	99.9	—	Unverified
6	HETMM	Detection AUROC	99.8	—	Unverified
7	INP-Fomer ViT-L (model-unified multi-class)	Detection AUROC	99.8	—	Unverified
8	EfficientAD (early stopping)	Detection AUROC	99.8	—	Unverified
9	DDAD	Detection AUROC	99.8	—	Unverified
10	PBAS	Detection AUROC	99.8	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	UniNet	Detection AUROC	99.8	—	Unverified
2	GLAD	Detection AUROC	99.5	—	Unverified
3	UniNet(model-unified multi-class)	Detection AUROC	99.15	—	Unverified
4	INP-Former ViT-B (model-unified multi-class)	Detection AUROC	98.9	—	Unverified
5	DDAD	Detection AUROC	98.9	—	Unverified
6	Dinomaly ViT-L (model-unified multi-class)	Detection AUROC	98.9	—	Unverified
7	DiffusionAD	Detection AUROC	98.8	—	Unverified
8	GLASS	Detection AUROC	98.8	—	Unverified
9	TransFusion	Detection AUROC	98.7	—	Unverified
10	HETMM	Detection AUROC	98.1	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	CSAD	Avg. Detection AUROC	95.3	—	Unverified
2	PSAD	Avg. Detection AUROC	94.9	—	Unverified