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 51–60 of 4856 papers

Title	Date	Tasks	Status	Hype
AdaptCLIP: Adapting CLIP for Universal Visual Anomaly Detection	May 15, 2025	Anomaly Detection	CodeCode Available	2
MetaUAS: Universal Anomaly Segmentation with One-Prompt Meta-Learning	May 14, 2025	Anomaly DetectionAnomaly Segmentation	CodeCode Available	2
Learning to Detect Multi-class Anomalies with Just One Normal Image Prompt	May 14, 2025	Anomaly DetectionAnomaly Segmentation	CodeCode Available	2
Few-Shot Anomaly-Driven Generation for Anomaly Classification and Segmentation	May 14, 2025	Anomaly ClassificationAnomaly Detection	CodeCode Available	2
Boosting Global-Local Feature Matching via Anomaly Synthesis for Multi-Class Point Cloud Anomaly Detection	May 12, 2025	Anomaly Detection	CodeCode Available	2
ReplayCAD: Generative Diffusion Replay for Continual Anomaly Detection	May 10, 2025	Anomaly Detectioncontinual anomaly detection	CodeCode Available	2
Detect, Classify, Act: Categorizing Industrial Anomalies with Multi-Modal Large Language Models	May 5, 2025	Anomaly ClassificationAnomaly Detection	CodeCode Available	2
CostFilter-AD: Enhancing Anomaly Detection through Matching Cost Filtering	May 2, 2025	Anomaly DetectionUnsupervised Anomaly Detection	CodeCode Available	2
ZClip: Adaptive Spike Mitigation for LLM Pre-Training	Apr 3, 2025	Anomaly DetectionLarge Language Model	CodeCode Available	2
Correcting Deviations from Normality: A Reformulated Diffusion Model for Multi-Class Unsupervised Anomaly Detection	Mar 25, 2025	Anomaly DetectionUnsupervised Anomaly Detection	CodeCode Available	2

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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