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 4301–4310 of 4856 papers

Title	Date	Tasks	Status
Modeling Heterogeneous Statistical Patterns in High-dimensional Data by Adversarial Distributions: An Unsupervised Generative Framework	Dec 15, 2020	Anomaly DetectionFraud Detection	CodeCode Available
Detecting Localized Density Anomalies in Multivariate Data via Coin-Flip Statistics	Mar 31, 2025	Anomaly DetectionComputational Efficiency	CodeCode Available
Detecting Irregular Network Activity with Adversarial Learning and Expert Feedback	Oct 1, 2022	Anomaly DetectionContrastive Learning	CodeCode Available
Hyperedge Anomaly Detection with Hypergraph Neural Network	Dec 7, 2024	Anomaly DetectionLink Prediction	CodeCode Available
Unsupervised Detection of Lesions in Brain MRI using constrained adversarial auto-encoders	Jun 13, 2018	Anomaly DetectionLesion Detection	CodeCode Available
BRUNO: A Deep Recurrent Model for Exchangeable Data	Feb 21, 2018	Anomaly DetectionBayesian Inference	CodeCode Available
Supervised Contrastive Learning for Detecting Anomalous Driving Behaviours from Multimodal Videos	Sep 9, 2021	Anomaly DetectionContrastive Learning	CodeCode Available
AnoOnly: Semi-Supervised Anomaly Detection with the Only Loss on Anomalies	May 30, 2023	Anomaly DetectionSemi-supervised Anomaly Detection	CodeCode Available
ANOMIX: A Simple yet Effective Hard Negative Generation via Mixing for Graph Anomaly Detection	Oct 27, 2024	Anomaly DetectionContrastive Learning	CodeCode Available
Monte Carlo EM for Deep Time Series Anomaly Detection	Dec 29, 2021	Anomaly DetectionTime Series	CodeCode Available

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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	UniNet	Detection AUROC	99.9	—	Unverified
5	GLASS	Detection AUROC	99.9	—	Unverified
6	PBAS	Detection AUROC	99.8	—	Unverified
7	HETMM	Detection AUROC	99.8	—	Unverified
8	INP-Fomer ViT-L (model-unified multi-class)	Detection AUROC	99.8	—	Unverified
9	DDAD	Detection AUROC	99.8	—	Unverified
10	EfficientAD (early stopping)	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	Dinomaly ViT-L (model-unified multi-class)	Detection AUROC	98.9	—	Unverified
5	INP-Former ViT-B (model-unified multi-class)	Detection AUROC	98.9	—	Unverified
6	DDAD	Detection AUROC	98.9	—	Unverified
7	GLASS	Detection AUROC	98.8	—	Unverified
8	DiffusionAD	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