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 611–620 of 4856 papers

Title	Date	Tasks	Status	Hype
CFA: Coupled-hypersphere-based Feature Adaptation for Target-Oriented Anomaly Localization	Jun 9, 2022	Anomaly DetectionAnomaly Localization	CodeCode Available	1
Adaptive Model Pooling for Online Deep Anomaly Detection from a Complex Evolving Data Stream	Jun 9, 2022	Anomaly Detection	CodeCode Available	1
Dual-Distribution Discrepancy for Anomaly Detection in Chest X-Rays	Jun 8, 2022	Anomaly DetectionOne-Class Classification	CodeCode Available	1
TSFEDL: A Python Library for Time Series Spatio-Temporal Feature Extraction and Prediction using Deep Learning (with Appendices on Detailed Network Architectures and Experimental Cases of Study)	Jun 7, 2022	Anomaly DetectionTime Series	CodeCode Available	1
Position Encoding Enhanced Feature Mapping for Image Anomaly Detection	Jun 6, 2022	Anomaly DetectionPosition	CodeCode Available	1
Anomaly detection in surveillance videos using transformer based attention model	Jun 3, 2022	Anomaly DetectionAnomaly Detection In Surveillance Videos	CodeCode Available	1
Fake It Till You Make It: Towards Accurate Near-Distribution Novelty Detection	May 28, 2022	Anomaly DetectionNovelty Detection	CodeCode Available	1
Raising the Bar in Graph-level Anomaly Detection	May 27, 2022	Anomaly DetectionFraud Detection	CodeCode Available	1
Attention-based residual autoencoder for video anomaly detection	May 25, 2022	Anomaly DetectionAnomaly Detection In Surveillance Videos	CodeCode Available	1
Exposing Outlier Exposure: What Can Be Learned From Few, One, and Zero Outlier Images	May 23, 2022	Anomaly Detection	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