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 1811–1820 of 4856 papers

Title	Date	Tasks	Status
Exploiting Spatial-temporal Correlations for Video Anomaly Detection	Nov 2, 2022	Anomaly DetectionDiversity	—Unverified
Efficacy of Statistical and Artificial Intelligence-based False Information Cyberattack Detection Models for Connected Vehicles	Aug 2, 2021	Anomaly Detection	—Unverified
CXR-AD: Component X-ray Image Dataset for Industrial Anomaly Detection	May 6, 2025	Anomaly DetectionDefect Detection	—Unverified
Efficient and Scalable Structure Learning for Bayesian Networks: Algorithms and Applications	Dec 7, 2020	Anomaly DetectionExplainable Recommendation	—Unverified
Anomaly Detection with the Voronoi Diagram Evolutionary Algorithm	Oct 27, 2016	Anomaly DetectionGeneral Classification	—Unverified
Efficient anomaly detection using bipartite k-NN graphs	Dec 1, 2011	Anomaly Detection	—Unverified
Efficient Anomaly Detection Using Self-Supervised Multi-Cue Tasks	Nov 24, 2021	Anomaly DetectionColorization	—Unverified
Efficient Anomaly Detection via Matrix Sketching	Apr 9, 2018	Anomaly Detection	—Unverified
Industrial Anomaly Detection and Localization Using Weakly-Supervised Residual Transformers	Jun 6, 2023	Anomaly DetectionAnomaly Localization	—Unverified
A2Log: Attentive Augmented Log Anomaly Detection	Sep 20, 2021	Anomaly DetectionData Augmentation	—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	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