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 2871–2880 of 4856 papers

Title	Date	Tasks	Status	Hype
ASE: Anomaly Scoring Based Ensemble Learning for Imbalanced Datasets	Mar 21, 2022	Anomaly DetectionBinary Classification	—Unverified	0
Diverse Counterfactual Explanations for Anomaly Detection in Time Series	Mar 21, 2022	Anomaly Detectioncounterfactual	—Unverified	0
AnoViT: Unsupervised Anomaly Detection and Localization with Vision Transformer-based Encoder-Decoder	Mar 21, 2022	Anomaly DetectionAnomaly Localization	CodeCode Available	1
Subspace Modeling for Fast Out-Of-Distribution and Anomaly Detection	Mar 20, 2022	Anomaly DetectionCPU	—Unverified	0
Learning from Multiple Expert Annotators for Enhancing Anomaly Detection in Medical Image Analysis	Mar 20, 2022	Anomaly DetectionMedical Image Analysis	CodeCode Available	0
Machine Learning-based Anomaly Detection in Optical Fiber Monitoring	Mar 19, 2022	Anomaly DetectionBIG-bench Machine Learning	—Unverified	0
No Shifted Augmentations (NSA): compact distributions for robust self-supervised Anomaly Detection	Mar 19, 2022	Anomaly DetectionOut of Distribution (OOD) Detection	—Unverified	0
Anomaly Detection in Emails using Machine Learning and Header Information	Mar 19, 2022	Anomaly DetectionBIG-bench Machine Learning	CodeCode Available	1
Nonnegative-Constrained Joint Collaborative Representation with Union Dictionary for Hyperspectral Anomaly Detection	Mar 18, 2022	Anomaly Detection	CodeCode Available	1
The Analysis of Online Event Streams: Predicting the Next Activity for Anomaly Detection	Mar 17, 2022	Activity PredictionAnomaly Detection	CodeCode Available	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	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