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 1981–1990 of 4856 papers

Title	Date	Tasks	Status
An Adaptive Approach for Anomaly Detector Selection and Fine-Tuning in Time Series	Jul 18, 2019	Anomaly DetectionTime Series	—Unverified
Explaining Anomalies with Tensor Networks	May 6, 2025	Anomaly DetectionTensor Networks	—Unverified
Crowded Scene Analysis: A Survey	Feb 6, 2015	Anomaly DetectionSurvey	—Unverified
Anomaly Detection with Prototype-Guided Discriminative Latent Embeddings	Apr 30, 2021	Anomaly DetectionOptical Flow Estimation	—Unverified
Few-shot Message-Enhanced Contrastive Learning for Graph Anomaly Detection	Nov 17, 2023	Anomaly DetectionContrastive Learning	—Unverified
Cross-Modal Fusion and Attention Mechanism for Weakly Supervised Video Anomaly Detection	Dec 29, 2024	Anomaly DetectionGraph Attention	—Unverified
Exploiting Completeness and Uncertainty of Pseudo Labels for Weakly Supervised Video Anomaly Detection	Dec 8, 2022	Anomaly DetectionDiversity	—Unverified
Exploiting Epistemic Uncertainty of Anatomy Segmentation for Anomaly Detection in Retinal OCT	May 29, 2019	AnatomyAnomaly Detection	—Unverified
Exploiting Point-Language Models with Dual-Prompts for 3D Anomaly Detection	Feb 16, 2025	3D Anomaly DetectionAnomaly Detection	—Unverified
Few-Shot Defect Segmentation Leveraging Abundant Normal Training Samples Through Normal Background Regularization and Crop-and-Paste Operation	Jul 18, 2020	Anomaly DetectionBenchmarking	—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	HETMM	Detection AUROC	99.8	—	Unverified
7	INP-Fomer ViT-L (model-unified multi-class)	Detection AUROC	99.8	—	Unverified
8	EfficientAD (early stopping)	Detection AUROC	99.8	—	Unverified
9	DDAD	Detection AUROC	99.8	—	Unverified
10	PBAS	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	INP-Former ViT-B (model-unified multi-class)	Detection AUROC	98.9	—	Unverified
5	DDAD	Detection AUROC	98.9	—	Unverified
6	Dinomaly ViT-L (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