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 2141–2150 of 4856 papers

Title	Date	Tasks	Status
Generation is better than Modification: Combating High Class Homophily Variance in Graph Anomaly Detection	Mar 15, 2024	Anomaly DetectionGraph Anomaly Detection	—Unverified
A Modified Dynamic Time Warping (MDTW) Approach and Innovative Average Non-Self Match Distance (ANSD) Method for Anomaly Detection in ECG Recordings	Nov 1, 2021	Anomaly DetectionDynamic Time Warping	—Unverified
Contextual-Bandit Anomaly Detection for IoT Data in Distributed Hierarchical Edge Computing	Apr 15, 2020	Anomaly DetectionEdge-computing	—Unverified
Generating Synthetic X-ray Images of a Person from the Surface Geometry	May 1, 2018	AnatomyAnomaly Detection	—Unverified
Generation of Anonymous Chest Radiographs Using Latent Diffusion Models for Training Thoracic Abnormality Classification Systems	Nov 2, 2022	Anomaly DetectionImage Generation	—Unverified
Generative Adversarial Evasion and Out-of-Distribution Detection for UAV Cyber-Attacks	Jun 26, 2025	Anomaly DetectionGenerative Adversarial Network	—Unverified
Contextual Affinity Distillation for Image Anomaly Detection	Jul 6, 2023	Anomaly DetectionKnowledge Distillation	—Unverified
Anomaly Detection Using One-Class SVM for Logs of Juniper Router Devices	May 21, 2023	Anomaly Detection	—Unverified
Generating and Reweighting Dense Contrastive Patterns for Unsupervised Anomaly Detection	Dec 26, 2023	Anomaly DetectionUnsupervised Anomaly Detection	—Unverified
A Novel Spatiotemporal Correlation Anomaly Detection Method Based on Time-Frequency-Domain Feature Fusion and a Dynamic Graph Neural Network in Wireless Sensor Network	Feb 25, 2025	Anomaly DetectionGraph Neural Network	—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