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 1341–1350 of 4856 papers

Title	Date	Tasks	Status	Hype
Learning Unified Reference Representation for Unsupervised Multi-class Anomaly Detection	Mar 18, 2024	Anomaly DetectionMulti-class Anomaly Detection	CodeCode Available	1
Out-of-Distribution Detection Should Use Conformal Prediction (and Vice-versa?)	Mar 18, 2024	Anomaly DetectionConformal Prediction	—Unverified	0
Binary Noise for Binary Tasks: Masked Bernoulli Diffusion for Unsupervised Anomaly Detection	Mar 18, 2024	Anomaly DetectionDenoising	CodeCode Available	1
usfAD Based Effective Unknown Attack Detection Focused IDS Framework	Mar 17, 2024	Anomaly DetectionIntrusion Detection	—Unverified	0
Tokensome: Towards a Genetic Vision-Language GPT for Explainable and Cognitive Karyotyping	Mar 17, 2024	Anomaly DetectionDecision Making	—Unverified	0
Customizing Visual-Language Foundation Models for Multi-modal Anomaly Detection and Reasoning	Mar 17, 2024	Anomaly Detection	CodeCode Available	1
Causality from Bottom to Top: A Survey	Mar 17, 2024	Anomaly DetectionFraud Detection	—Unverified	0
Anomaly Detection Based on Isolation Mechanisms: A Survey	Mar 16, 2024	Anomaly DetectionSurvey	—Unverified	0
DTOR: Decision Tree Outlier Regressor to explain anomalies	Mar 16, 2024	Anomaly Detectionvalid	CodeCode Available	0
Generation is better than Modification: Combating High Class Homophily Variance in Graph Anomaly Detection	Mar 15, 2024	Anomaly DetectionGraph Anomaly Detection	—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