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 2381–2390 of 4856 papers

Title	Date	Tasks	Status	Hype
Exploiting Completeness and Uncertainty of Pseudo Labels for Weakly Supervised Video Anomaly Detection	Dec 8, 2022	Anomaly DetectionDiversity	—Unverified	0
On Root Cause Localization and Anomaly Mitigation through Causal Inference	Dec 8, 2022	Anomaly DetectionCausal Inference	CodeCode Available	1
Unsupervised Anomaly Detection in Time-series: An Extensive Evaluation and Analysis of State-of-the-art Methods	Dec 6, 2022	Anomaly DetectionTime Series	—Unverified	0
Prototypical Residual Networks for Anomaly Detection and Localization	Dec 5, 2022	Anomaly DetectionSupervised Anomaly Detection	—Unverified	0
Lossy Compression for Robust Unsupervised Time-Series Anomaly Detection	Dec 5, 2022	Anomaly DetectionTime Series	—Unverified	0
Continual learning on deployment pipelines for Machine Learning Systems	Dec 5, 2022	Anomaly DetectionContinual Learning	—Unverified	0
Anomaly Detection in Power Markets and Systems	Dec 5, 2022	Anomaly Detection	—Unverified	0
AIDA: Analytic Isolation and Distance-based Anomaly Detection Algorithm	Dec 5, 2022	Anomaly Detection	CodeCode Available	0
FEMa-FS: Finite Element Machines for Feature Selection	Dec 5, 2022	Anomaly Detectionfeature selection	—Unverified	0
Application of a Dynamic Line Graph Neural Network for Intrusion Detection With Semisupervised Learning	Dec 2, 2022	Anomaly DetectionGraph Neural Network	—Unverified	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	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