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 1171–1180 of 4856 papers

Title	Date	Tasks	Status
Abnormal activity capture from passenger flow of elevator based on unsupervised learning and fine-grained multi-label recognition	Jun 29, 2020	Anomaly DetectionClustering	—Unverified
Recent Advances in Diffusion Models for Hyperspectral Image Processing and Analysis: A Review	May 16, 2025	Anomaly DetectionDenoising	—Unverified
A Survey on Explainable Anomaly Detection	Oct 13, 2022	Anomaly DetectionSurvey	—Unverified
A Survey on Embedding Dynamic Graphs	Jan 4, 2021	Anomaly DetectionDynamic graph embedding	—Unverified
A Survey on Incorporating Domain Knowledge into Deep Learning for Medical Image Analysis	Apr 25, 2020	Anomaly DetectionDiagnostic	—Unverified
Active anomaly detection based on deep one-class classification	Sep 18, 2023	Active LearningAnomaly Detection	—Unverified
CRCL: Causal Representation Consistency Learning for Anomaly Detection in Surveillance Videos	Mar 24, 2025	Anomaly DetectionAnomaly Detection In Surveillance Videos	—Unverified
A Survey on Deep Learning Techniques for Video Anomaly Detection	Sep 29, 2020	Anomaly DetectionDeep Learning	—Unverified
A Survey on Anomaly Detection for Technical Systems using LSTM Networks	May 28, 2021	Anomaly DetectionSurvey	—Unverified
Anomaly-Aware Semantic Segmentation via Style-Aligned OoD Augmentation	Aug 19, 2023	Anomaly DetectionAnomaly Segmentation	—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