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 541–550 of 4856 papers

Title	Date	Tasks	Status	Hype
U-Flow: A U-shaped Normalizing Flow for Anomaly Detection with Unsupervised Threshold	Nov 22, 2022	Anomaly DetectionAnomaly Segmentation	CodeCode Available	1
DeSTSeg: Segmentation Guided Denoising Student-Teacher for Anomaly Detection	Nov 21, 2022	Anomaly DetectionDecoder	CodeCode Available	1
Normalizing Flows for Human Pose Anomaly Detection	Nov 20, 2022	Abnormal Event Detection In VideoAnomaly Detection	CodeCode Available	1
Are we certain it's anomalous?	Nov 16, 2022	Anomaly DetectionTime Series	CodeCode Available	1
Anomaly Detection in Multiplex Dynamic Networks: from Blockchain Security to Brain Disease Prediction	Nov 15, 2022	Anomaly DetectionDisease Prediction	CodeCode Available	1
FAPM: Fast Adaptive Patch Memory for Real-time Industrial Anomaly Detection	Nov 14, 2022	Anomaly Detection	CodeCode Available	1
LGN-Net: Local-Global Normality Network for Video Anomaly Detection	Nov 14, 2022	Anomaly DetectionVideo Anomaly Detection	CodeCode Available	1
Far Away in the Deep Space: Dense Nearest-Neighbor-Based Out-of-Distribution Detection	Nov 12, 2022	Anomaly DetectionDensity Estimation	CodeCode Available	1
Deep Learning for Time Series Anomaly Detection: A Survey	Nov 9, 2022	Anomaly DetectionDeep Learning	CodeCode Available	1
A Comprehensive Survey of Regression Based Loss Functions for Time Series Forecasting	Nov 5, 2022	Anomaly Detectionregression	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