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 241–250 of 4856 papers

Title	Date	Tasks	Status	Hype
MultiADS: Defect-aware Supervision for Multi-type Anomaly Detection and Segmentation in Zero-Shot Learning	Apr 9, 2025	Anomaly DetectionAnomaly Segmentation	—Unverified	0
AMAD: AutoMasked Attention for Unsupervised Multivariate Time Series Anomaly Detection	Apr 9, 2025	Anomaly DetectionContrastive Learning	—Unverified	0
Reconstruction-Free Anomaly Detection with Diffusion Models via Direct Latent Likelihood Evaluation	Apr 8, 2025	Anomaly Detection	CodeCode Available	1
Autoencoder-Based Detection of Anomalous Stokes V Spectra in the Flare-Producing Active Region 13663 Using Hinode/SP Observations	Apr 8, 2025	Anomaly Detection	—Unverified	0
A Self-Supervised Framework for Space Object Behaviour Characterisation	Apr 8, 2025	Anomaly DetectionEarth Observation	—Unverified	0
Hybrid Temporal Differential Consistency Autoencoder for Efficient and Sustainable Anomaly Detection in Cyber-Physical Systems	Apr 8, 2025	Anomaly DetectionComputational Efficiency	—Unverified	0
System Log Parsing with Large Language Models: A Review	Apr 7, 2025	Anomaly DetectionLog Parsing	CodeCode Available	1
Towards Efficient Real-Time Video Motion Transfer via Generative Time Series Modeling	Apr 7, 2025	Anomaly DetectionOptical Flow Estimation	—Unverified	0
On multipolar magnetic anomaly detection: multipolar signal subspaces, an analytical orthonormal basis, multipolar truncature and detection performance	Apr 7, 2025	Anomaly Detection	—Unverified	0
iADCPS: Time Series Anomaly Detection for Evolving Cyber-physical Systems via Incremental Meta-learning	Apr 6, 2025	Anomaly DetectionMeta-Learning	—Unverified	0

Show:10 25 50

← PrevPage 25 of 486Next →

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