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 1231–1240 of 4856 papers

Title	Date	Tasks	Status	Score
An AI System for Continuous Knee Osteoarthritis Severity Grading Using Self-Supervised Anomaly Detection with Limited Data	Jul 16, 2024	Anomaly DetectionDenoising	CodeCode Available	5
Generator Based Inference (GBI)	May 30, 2025	Anomaly Detectionparameter estimation	CodeCode Available	5
A Deep Recurrent-Reinforcement Learning Method for Intelligent AutoScaling of Serverless Functions	Aug 11, 2023	Anomaly DetectionCPU	CodeCode Available	5
Anomaly Detection with Selective Dictionary Learning	Jul 17, 2023	Anomaly DetectionDictionary Learning	CodeCode Available	5
Generative Neural Networks for Anomaly Detection in Crowded Scenes	Oct 29, 2018	Abnormal Event Detection In VideoAnomaly Detection	CodeCode Available	5
Generative Optimization Networks for Memory Efficient Data Generation	Oct 6, 2021	Anomaly DetectionTime Series	CodeCode Available	5
A Deep Probabilistic Framework for Continuous Time Dynamic Graph Generation	Dec 20, 2024	Anomaly DetectionData Augmentation	CodeCode Available	5
GLADMamba: Unsupervised Graph-Level Anomaly Detection Powered by Selective State Space Model	Mar 23, 2025	Anomaly DetectionDrug Discovery	CodeCode Available	5
Anomaly Detection with Robust Deep Autoencoders	Aug 13, 2017	Anomaly DetectionDenoising	CodeCode Available	5
A Deep Neural Network for Unsupervised Anomaly Detection and Diagnosis in Multivariate Time Series Data	Nov 20, 2018	Anomaly DetectionDecoder	CodeCode Available	5

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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	INP-Fomer ViT-L (model-unified multi-class)	Detection AUROC	99.8	—	Unverified
7	DDAD	Detection AUROC	99.8	—	Unverified
8	EfficientAD (early stopping)	Detection AUROC	99.8	—	Unverified
9	PBAS	Detection AUROC	99.8	—	Unverified
10	HETMM	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	DDAD	Detection AUROC	98.9	—	Unverified
5	Dinomaly ViT-L (model-unified multi-class)	Detection AUROC	98.9	—	Unverified
6	INP-Former ViT-B (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