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 2661–2670 of 4856 papers

Title	Date	Tasks	Status
Brain Surface Reconstruction from MRI Images Based on Segmentation Networks Applying Signed Distance Maps	Apr 9, 2021	Anomaly DetectionSkull Stripping	—Unverified
Brain Tumor Anomaly Detection via Latent Regularized Adversarial Network	Jul 9, 2020	Anomaly DetectionSemi-supervised Anomaly Detection	—Unverified
Breaking the Bias: Recalibrating the Attention of Industrial Anomaly Detection	Dec 11, 2024	Anomaly DetectionComputational Efficiency	—Unverified
Bridge Feature Matching and Cross-Modal Alignment with Mutual-filtering for Zero-shot Anomaly Detection	Jul 15, 2025	Anomaly ClassificationAnomaly Detection	—Unverified
Bridging Machine Learning and Sciences: Opportunities and Challenges	Oct 24, 2022	Anomaly DetectionOut-of-Distribution Detection	—Unverified
Bridging Unsupervised and Semi-Supervised Anomaly Detection: A Theoretically-Grounded and Practical Framework with Synthetic Anomalies	Jun 16, 2025	Anomaly DetectionSemi-supervised Anomaly Detection	—Unverified
Brittle Features May Help Anomaly Detection	Apr 21, 2021	Anomaly DetectionKnowledge Distillation	—Unverified
BSSAD: Towards A Novel Bayesian State-Space Approach for Anomaly Detection in Multivariate Time Series	Jan 30, 2023	Anomaly DetectionState Space Models	—Unverified
Building Machine Learning Challenges for Anomaly Detection in Science	Mar 3, 2025	Anomaly Detectionscientific discovery	—Unverified
Burnt area extraction from high-resolution satellite images based on anomaly detection	Aug 25, 2023	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