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 3201–3210 of 4856 papers

Title	Date	Tasks	Status
Subgraph2vec: A random walk-based algorithm for embedding knowledge graphs	May 3, 2024	Anomaly DetectionDecision Making	—Unverified
Subset Multivariate Collective And Point Anomaly Detection	Sep 4, 2019	Anomaly Detection	—Unverified
Subspace Modeling for Fast Out-Of-Distribution and Anomaly Detection	Mar 20, 2022	Anomaly DetectionCPU	—Unverified
Successive Convex Approximation Algorithms for Sparse Signal Estimation with Nonconvex Regularizations	Jun 28, 2018	Anomaly DetectionClustering	—Unverified
Successive Data Injection in Conditional Quantum GAN Applied to Time Series Anomaly Detection	Oct 8, 2023	Anomaly DetectionTime Series	—Unverified
SunDown: Model-driven Per-Panel Solar Anomaly Detection for Residential Arrays	May 25, 2020	Anomaly ClassificationAnomaly Detection	—Unverified
SuperAD: A Training-free Anomaly Classification and Segmentation Method for CVPR 2025 VAND 3.0 Workshop Challenge Track 1: Adapt & Detect	May 26, 2025	Anomaly ClassificationAnomaly Detection	—Unverified
Supervised Anomaly Detection based on Deep Autoregressive Density Estimators	Apr 12, 2019	Anomaly DetectionDensity Estimation	—Unverified
Supervised Anomaly Detection in Uncertain Pseudoperiodic Data Streams	Jul 20, 2016	Anomaly DetectionComputational Efficiency	—Unverified
Supervised Anomaly Detection Method Combining Generative Adversarial Networks and Three-Dimensional Data in Vehicle Inspections	Dec 22, 2022	Anomaly DetectionSupervised Anomaly Detection	—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