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	Score
A Comprehensive Survey on Graph Anomaly Detection with Deep Learning	Jun 14, 2021	Anomaly DetectionDeep Learning	CodeCode Available	1	5
A Principled Approach to Enriching Security-related Data for Running Processes through Statistics and Natural Language Processing	Apr 11, 2021	Anomaly Detection	CodeCode Available	1	5
ADA-GAD: Anomaly-Denoised Autoencoders for Graph Anomaly Detection	Dec 22, 2023	Anomaly DetectionFraud Detection	CodeCode Available	1	5
Demystifying and Extracting Fault-indicating Information from Logs for Failure Diagnosis	Sep 20, 2024	Anomaly DetectionFault Diagnosis	CodeCode Available	1	5
AnomalyR1: A GRPO-based End-to-end MLLM for Industrial Anomaly Detection	Apr 16, 2025	Anomaly DetectionLarge Language Model	CodeCode Available	1	5
DenseHybrid: Hybrid Anomaly Detection for Dense Open-set Recognition	Jul 6, 2022	Anomaly DetectionOpen Set Learning	CodeCode Available	1	5
AUPIMO: Redefining Visual Anomaly Detection Benchmarks with High Speed and Low Tolerance	Jan 3, 2024	Anomaly Detection	CodeCode Available	1	5
COVID-19-CT-CXR: a freely accessible and weakly labeled chest X-ray and CT image collection on COVID-19 from biomedical literature	Jun 11, 2020	Anomaly DetectionArticles	CodeCode Available	1	5
A Two-Stage Generative Model with CycleGAN and Joint Diffusion for MRI-based Brain Tumor Detection	Nov 6, 2023	Anomaly DetectionGenerative Adversarial Network	CodeCode Available	1	5
Cross-Modal Learning for Anomaly Detection in Complex Industrial Process: Methodology and Benchmark	Jun 13, 2024	Anomaly DetectionARC	CodeCode Available	1	5

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