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 2801–2810 of 4856 papers

Title	Date	Tasks	Status
Spatial-Temporal Anomaly Detection for Sensor Attacks in Autonomous Vehicles	Dec 15, 2022	Anomaly DetectionAutonomous Vehicles	—Unverified
Cross-Domain Video Anomaly Detection without Target Domain Adaptation	Dec 14, 2022	Anomaly DetectionDomain Adaptation	—Unverified
Mul-GAD: a semi-supervised graph anomaly detection framework via aggregating multi-view information	Dec 11, 2022	Anomaly DetectionGraph Anomaly Detection	CodeCode Available
Matrix Profile XXVII: A Novel Distance Measure for Comparing Long Time Series	Dec 9, 2022	Anomaly DetectionDynamic Time Warping	—Unverified
Exploiting Completeness and Uncertainty of Pseudo Labels for Weakly Supervised Video Anomaly Detection	Dec 8, 2022	Anomaly DetectionDiversity	—Unverified
Transformer-based normative modelling for anomaly detection of early schizophrenia	Dec 8, 2022	Anomaly Detection	—Unverified
Unsupervised Anomaly Detection in Time-series: An Extensive Evaluation and Analysis of State-of-the-art Methods	Dec 6, 2022	Anomaly DetectionTime Series	—Unverified
AIDA: Analytic Isolation and Distance-based Anomaly Detection Algorithm	Dec 5, 2022	Anomaly Detection	CodeCode Available
Prototypical Residual Networks for Anomaly Detection and Localization	Dec 5, 2022	Anomaly DetectionSupervised Anomaly Detection	—Unverified
Continual learning on deployment pipelines for Machine Learning Systems	Dec 5, 2022	Anomaly DetectionContinual Learning	—Unverified

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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