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 131–140 of 4856 papers

Title	Date	Tasks	Status	Hype	Score
CSAD: Unsupervised Component Segmentation for Logical Anomaly Detection	Aug 28, 2024	Anomaly DetectionSegmentation	CodeCode Available	2	5
Correcting Deviations from Normality: A Reformulated Diffusion Model for Multi-Class Unsupervised Anomaly Detection	Mar 25, 2025	Anomaly DetectionUnsupervised Anomaly Detection	CodeCode Available	2	5
CATCH: Channel-Aware multivariate Time Series Anomaly Detection via Frequency Patching	Oct 16, 2024	Anomaly DetectionTime Series	CodeCode Available	2	5
CostFilter-AD: Enhancing Anomaly Detection through Matching Cost Filtering	May 2, 2025	Anomaly DetectionUnsupervised Anomaly Detection	CodeCode Available	2	5
Boosting Global-Local Feature Matching via Anomaly Synthesis for Multi-Class Point Cloud Anomaly Detection	Feb 21, 2025	3D Anomaly Detection3D Anomaly Detection and Segmentation	CodeCode Available	2	5
Detect, Classify, Act: Categorizing Industrial Anomalies with Multi-Modal Large Language Models	May 5, 2025	Anomaly ClassificationAnomaly Detection	CodeCode Available	2	5
AnomalyDINO: Boosting Patch-based Few-shot Anomaly Detection with DINOv2	May 23, 2024	Anomaly DetectionAnomaly Segmentation	CodeCode Available	2	5
DiAD: A Diffusion-based Framework for Multi-class Anomaly Detection	Dec 11, 2023	Anomaly DetectionDenoising	CodeCode Available	2	5
Anomaly Detection via Reverse Distillation from One-Class Embedding	Jan 26, 2022	Anomaly Classification	CodeCode Available	2	5
A Survey on RGB, 3D, and Multimodal Approaches for Unsupervised Industrial Anomaly Detection	Oct 29, 2024	Anomaly Detection	CodeCode Available	2	5

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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