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 3401–3410 of 4856 papers

Title	Date	Tasks	Status
Uncertainty-aware Human Mobility Modeling and Anomaly Detection	Oct 2, 2024	Anomaly DetectionDecision Making	—Unverified
Unconditional Scene Graph Generation	Aug 12, 2021	Anomaly DetectionGraph Generation	—Unverified
Uncovering Issues in the Radio Access Network by Looking at the Neighbors	Apr 20, 2025	Anomaly DetectionContextual Anomaly Detection	—Unverified
Understanding Ethics, Privacy, and Regulations in Smart Video Surveillance for Public Safety	Dec 25, 2022	Action RecognitionAnomaly Detection	—Unverified
Understanding Parameter Saliency via Extreme Value Theory	Oct 27, 2023	Anomaly DetectionSaliency Ranking	—Unverified
Understanding Policy and Technical Aspects of AI-Enabled Smart Video Surveillance to Address Public Safety	Feb 8, 2023	Action DetectionAnomaly Detection	—Unverified
Understanding the Challenges and Opportunities of Pose-based Anomaly Detection	Mar 9, 2023	Anomaly DetectionPose-based Anomaly Detection	—Unverified
A Robust Interpretable Deep Learning Classifier for Heart Anomaly Detection Without Segmentation	May 21, 2020	Anomaly DetectionClassification	—Unverified
Understanding the limitations of self-supervised learning for tabular anomaly detection	Sep 15, 2023	Anomaly DetectionSelf-Supervised Learning	—Unverified
Understanding Time Series Anomaly State Detection through One-Class Classification	Feb 3, 2024	Anomaly DetectionOne-Class Classification	—Unverified

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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	INP-Fomer ViT-L (model-unified multi-class)	Detection AUROC	99.8	—	Unverified
7	DDAD	Detection AUROC	99.8	—	Unverified
8	EfficientAD (early stopping)	Detection AUROC	99.8	—	Unverified
9	PBAS	Detection AUROC	99.8	—	Unverified
10	HETMM	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	DDAD	Detection AUROC	98.9	—	Unverified
5	Dinomaly ViT-L (model-unified multi-class)	Detection AUROC	98.9	—	Unverified
6	INP-Former ViT-B (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