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 2621–2630 of 4856 papers

Title	Date	Tasks	Status
Bagged Regularized k-Distances for Anomaly Detection	Dec 2, 2023	Anomaly DetectionDensity Estimation	—Unverified
Balancing Privacy and Action Performance: A Penalty-Driven Approach to Image Anonymization	Apr 19, 2025	Action RecognitionActivity Recognition	—Unverified
Ball Mill Fault Prediction Based on Deep Convolutional Auto-Encoding Network	Nov 9, 2023	Anomaly DetectionFault Detection	—Unverified
Batch Uniformization for Minimizing Maximum Anomaly Score of DNN-based Anomaly Detection in Sounds	Jul 19, 2019	Anomaly DetectionDensity Estimation	—Unverified
Battery Cloud with Advanced Algorithms	Mar 7, 2022	Anomaly DetectionManagement	—Unverified
Battery State of Health Estimation Using LLM Framework	Jan 30, 2025	Anomaly Detection	—Unverified
Bayesian Anomaly Detection and Classification	Feb 22, 2019	Anomaly DetectionClassification	—Unverified
Bayesian Anomaly Detection Using Extreme Value Theory	May 29, 2019	Anomaly DetectionClustering	—Unverified
Bayesian autoencoders with uncertainty quantification: Towards trustworthy anomaly detection	Feb 25, 2022	Anomaly DetectionUncertainty Quantification	—Unverified
Bayesian generative models can flag performance loss, bias, and out-of-distribution image content	Mar 21, 2025	Anomaly DetectionData Visualization	—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