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 3531–3540 of 4856 papers

Title	Date	Tasks	Status
Affine-Invariant Integrated Rank-Weighted Depth: Definition, Properties and Finite Sample Analysis	Jun 21, 2021	Anomaly Detection	—Unverified
Skeleton-based human action evaluation using graph convolutional network for monitoring Alzheimer’s progression	Jun 21, 2021	Action AssessmentAnomaly Detection	—Unverified
Spliced Binned-Pareto Distribution for Robust Modeling of Heavy-tailed Time Series	Jun 21, 2021	Anomaly DetectionTime Series	CodeCode Available
Low-rank Characteristic Tensor Density Estimation Part II: Compression and Latent Density Estimation	Jun 20, 2021	Anomaly DetectionDensity Estimation	CodeCode Available
Large-Scale Network Embedding in Apache Spark	Jun 20, 2021	Anomaly DetectionLink Prediction	—Unverified
Glancing at the Patch: Anomaly Localization With Global and Local Feature Comparison	Jun 19, 2021	Anomaly DetectionAnomaly Localization	—Unverified
BinarizedAttack: Structural Poisoning Attacks to Graph-based Anomaly Detection	Jun 18, 2021	Anomaly DetectionCombinatorial Optimization	CodeCode Available
Federated Learning for Intrusion Detection System: Concepts, Challenges and Future Directions	Jun 16, 2021	Anomaly DetectionAutonomous Vehicles	—Unverified
Anomaly Detection and Automated Labeling for Voter Registration File Changes	Jun 16, 2021	Anomaly DetectionUnsupervised Anomaly Detection	—Unverified
X-MAN: Explaining multiple sources of anomalies in video	Jun 16, 2021	Anomaly DetectionDecision Making	—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	UniNet	Detection AUROC	99.9	—	Unverified
5	GLASS	Detection AUROC	99.9	—	Unverified
6	PBAS	Detection AUROC	99.8	—	Unverified
7	HETMM	Detection AUROC	99.8	—	Unverified
8	INP-Fomer ViT-L (model-unified multi-class)	Detection AUROC	99.8	—	Unverified
9	DDAD	Detection AUROC	99.8	—	Unverified
10	EfficientAD (early stopping)	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	Dinomaly ViT-L (model-unified multi-class)	Detection AUROC	98.9	—	Unverified
5	INP-Former ViT-B (model-unified multi-class)	Detection AUROC	98.9	—	Unverified
6	DDAD	Detection AUROC	98.9	—	Unverified
7	GLASS	Detection AUROC	98.8	—	Unverified
8	DiffusionAD	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