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 901–910 of 4856 papers

Title	Date	Tasks	Status	Hype	Score
UBnormal: New Benchmark for Supervised Open-Set Video Anomaly Detection	Nov 16, 2021	Action RecognitionAnomaly Detection	CodeCode Available	1	5
ICSML: Industrial Control Systems ML Framework for native inference using IEC 61131-3 code	Feb 21, 2022	Anomaly Detection	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
Learning Not to Reconstruct Anomalies	Oct 19, 2021	Anomaly DetectionOne-Class Classification	CodeCode Available	1	5
LGN-Net: Local-Global Normality Network for Video Anomaly Detection	Nov 14, 2022	Anomaly DetectionVideo Anomaly Detection	CodeCode Available	1	5
Uncertainty-Weighted Image-Event Multimodal Fusion for Video Anomaly Detection	May 5, 2025	Anomaly DetectionAnomaly Detection In Surveillance Videos	CodeCode Available	1	5
LUNAR: Unifying Local Outlier Detection Methods via Graph Neural Networks	Dec 10, 2021	Anomaly DetectionGraph Neural Network	CodeCode Available	1	5
ImbSAM: A Closer Look at Sharpness-Aware Minimization in Class-Imbalanced Recognition	Aug 15, 2023	Anomaly DetectionSemi-supervised Anomaly Detection	CodeCode Available	1	5
ImDiffusion: Imputed Diffusion Models for Multivariate Time Series Anomaly Detection	Jul 3, 2023	Anomaly DetectionImputation	CodeCode Available	1	5
Meta-AAD: Active Anomaly Detection with Deep Reinforcement Learning	Sep 16, 2020	Anomaly DetectionDeep Reinforcement Learning	CodeCode Available	1	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	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