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 91–100 of 4856 papers

Title	Date	Tasks	Status	Hype
SuperAD: A Training-free Anomaly Classification and Segmentation Method for CVPR 2025 VAND 3.0 Workshop Challenge Track 1: Adapt & Detect	May 26, 2025	Anomaly ClassificationAnomaly Detection	—Unverified	0
Vad-R1: Towards Video Anomaly Reasoning via Perception-to-Cognition Chain-of-Thought	May 26, 2025	Anomaly DetectionVideo Anomaly Detection	CodeCode Available	1
eACGM: Non-instrumented Performance Tracing and Anomaly Detection towards Machine Learning Systems	May 25, 2025	Anomaly DetectionFault Diagnosis	—Unverified	0
Words as Geometric Features: Estimating Homography using Optical Character Recognition as Compressed Image Representation	May 25, 2025	Anomaly DetectionHomography Estimation	—Unverified	0
Rethinking Metrics and Benchmarks of Video Anomaly Detection	May 25, 2025	Anomaly DetectionVideo Anomaly Detection	—Unverified	0
Chi-Square Wavelet Graph Neural Networks for Heterogeneous Graph Anomaly Detection	May 25, 2025	Anomaly DetectionGraph Anomaly Detection	CodeCode Available	0
Anomaly detection in radio galaxy data with trainable COSFIRE filters	May 24, 2025	Anomaly DetectionAstronomy	CodeCode Available	0
Rethinking Contrastive Learning in Graph Anomaly Detection: A Clean-View Perspective	May 23, 2025	Anomaly DetectionContrastive Learning	—Unverified	0
Center-aware Residual Anomaly Synthesis for Multi-class Industrial Anomaly Detection	May 23, 2025	Anomaly DetectionMulti-class Anomaly Detection	CodeCode Available	1
Learning Normal Patterns in Musical Loops	May 22, 2025	Anomaly DetectionInformation Retrieval	—Unverified	0

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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