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 1321–1330 of 4856 papers

Title	Date	Tasks	Status
A Rank-SVM Approach to Anomaly Detection	May 2, 2014	Anomaly DetectionComputational Efficiency	—Unverified
A Random Matrix Theoretical Approach to Early Event Detection in Smart Grid	Jan 31, 2015	Anomaly DetectionDecision Making	—Unverified
A New Comprehensive Benchmark for Semi-supervised Video Anomaly Detection and Anticipation	May 23, 2023	Anomaly DetectionVideo Anomaly Detection	—Unverified
ADT: Agent-based Dynamic Thresholding for Anomaly Detection	Dec 3, 2023	Anomaly Detection	—Unverified
Preference Isolation Forest for Structure-based Anomaly Detection	May 16, 2025	Anomaly Detection	—Unverified
Deep Learning for the Analysis of Disruption Precursors based on Plasma Tomography	Sep 6, 2020	Anomaly Detection	—Unverified
A New Approach to Dimensionality Reduction for Anomaly Detection in Data Traffic	Jun 14, 2016	Anomaly DetectionDimensionality Reduction	—Unverified
A Probabilistic Framework to Node-level Anomaly Detection in Communication Networks	Feb 12, 2019	Anomaly Detection	—Unverified
ADS-ME: Anomaly Detection System for Micro-expression Spotting	Mar 11, 2019	Anomaly DetectionMicro-Expression Spotting	—Unverified
A Privacy-Preserving Framework for Advertising Personalization Incorporating Federated Learning and Differential Privacy	Jul 16, 2025	Anomaly DetectionFederated Learning	—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