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 211–220 of 4856 papers

Title	Date	Tasks	Status	Hype
A Critical Review of Common Log Data Sets Used for Evaluation of Sequence-based Anomaly Detection Techniques	Sep 6, 2023	Anomaly Detection	CodeCode Available	1
Combining GANs and AutoEncoders for Efficient Anomaly Detection	Nov 16, 2020	Adversarial AttackAnomaly Detection	CodeCode Available	1
Component-aware anomaly detection framework for adjustable and logical industrial visual inspection	May 15, 2023	Anomaly ClassificationAnomaly Detection	CodeCode Available	1
Computer Vision for Clinical Gait Analysis: A Gait Abnormality Video Dataset	Jul 5, 2024	Action RecognitionAnomaly Detection	CodeCode Available	1
Cheating Depth: Enhancing 3D Surface Anomaly Detection via Depth Simulation	Nov 2, 2023	Anomaly DetectionDepth Anomaly Detection and Segmentation	CodeCode Available	1
CESNET-TimeSeries24: Time Series Dataset for Network Traffic Anomaly Detection and Forecasting	Sep 27, 2024	Anomaly DetectionTime Series	CodeCode Available	1
Continual Learning for Unsupervised Anomaly Detection in Continuous Auditing of Financial Accounting Data	Dec 25, 2021	Anomaly Detectioncontinual anomaly detection	CodeCode Available	1
A Shapelet-based Framework for Unsupervised Multivariate Time Series Representation Learning	May 30, 2023	Anomaly DetectionData Augmentation	CodeCode Available	1
Center-aware Residual Anomaly Synthesis for Multi-class Industrial Anomaly Detection	May 23, 2025	Anomaly DetectionMulti-class Anomaly Detection	CodeCode Available	1
CFA: Coupled-hypersphere-based Feature Adaptation for Target-Oriented Anomaly Localization	Jun 9, 2022	Anomaly DetectionAnomaly Localization	CodeCode Available	1

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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