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 601–610 of 4856 papers

Title	Date	Tasks	Status	Hype
Partial and Asymmetric Contrastive Learning for Out-of-Distribution Detection in Long-Tailed Recognition	Jul 4, 2022	Anomaly DetectionContrastive Learning	CodeCode Available	1
Deep Contrastive One-Class Time Series Anomaly Detection	Jul 4, 2022	Anomaly DetectionContrastive Learning	CodeCode Available	1
Multivariate Time Series Anomaly Detection with Few Positive Samples	Jul 2, 2022	Anomaly DetectionTime Series	CodeCode Available	1
Online Anomaly Detection Based On Reservoir Sampling and LOF for IoT devices	Jun 28, 2022	Anomaly Detection	CodeCode Available	1
Learning Deep Feature Correspondence for Unsupervised Anomaly Detection and Segmentation	Jun 27, 2022	Anomaly DetectionUnsupervised Anomaly Detection	CodeCode Available	1
Local Evaluation of Time Series Anomaly Detection Algorithms	Jun 27, 2022	Anomaly DetectionTime Series	CodeCode Available	1
Robustness Evaluation of Deep Unsupervised Learning Algorithms for Intrusion Detection Systems	Jun 25, 2022	Anomaly DetectionData Poisoning	CodeCode Available	1
Multi Visual Modality Fall Detection Dataset	Jun 25, 2022	Anomaly Detection	CodeCode Available	1
Anatomy-Guided Weakly-Supervised Abnormality Localization in Chest X-rays	Jun 25, 2022	AnatomyAnomaly Detection	CodeCode Available	1
Deep Isolation Forest for Anomaly Detection	Jun 14, 2022	Anomaly DetectionTime Series	CodeCode Available	1

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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