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 2181–2190 of 4856 papers

Title	Date	Tasks	Status
Are you sure it’s an artifact? Artifact detection and uncertainty quantification in histological images	Dec 23, 2023	Anomaly DetectionArtifact Detection	CodeCode Available
Understanding normalization in contrastive representation learning and out-of-distribution detection	Dec 23, 2023	Anomaly DetectionContrastive Learning	CodeCode Available
Progressing from Anomaly Detection to Automated Log Labeling and Pioneering Root Cause Analysis	Dec 22, 2023	Anomaly DetectionManagement	—Unverified
Generative Pretraining at Scale: Transformer-Based Encoding of Transactional Behavior for Fraud Detection	Dec 22, 2023	Anomaly DetectionFraud Detection	—Unverified
Self-supervised Complex Network for Machine Sound Anomaly Detection	Dec 21, 2023	Anomaly DetectionTime Series	—Unverified
Comparative Evaluation of Anomaly Detection Methods for Fraud Detection in Online Credit Card Payments	Dec 21, 2023	Anomaly DetectionFraud Detection	—Unverified
Fast kernel half-space depth for data with non-convex supports	Dec 21, 2023	Anomaly DetectionDescriptive	—Unverified
Machine Learning for Anomaly Detection in Particle Physics	Dec 20, 2023	Anomaly Detection	—Unverified
Evolutionary Optimization of 1D-CNN for Non-contact Respiration Pattern Classification	Dec 20, 2023	Anomaly DetectionClassification	—Unverified
CRD: Collaborative Representation Distance for Practical Anomaly Detection	Dec 20, 2023	Anomaly DetectionComputational Efficiency	—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	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