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 1761–1770 of 4856 papers

Title	Date	Tasks	Status
Anomaly Detection for Tabular Data with Internal Contrastive Learning	Sep 29, 2021	Anomaly DetectionContrastive Learning	—Unverified
Ball Mill Fault Prediction Based on Deep Convolutional Auto-Encoding Network	Nov 9, 2023	Anomaly DetectionFault Detection	—Unverified
Balancing Privacy and Action Performance: A Penalty-Driven Approach to Image Anonymization	Apr 19, 2025	Action RecognitionActivity Recognition	—Unverified
Anomaly Detection for Skin Disease Images Using Variational Autoencoder	Jul 3, 2018	Anomaly Detection	—Unverified
Bagged Regularized k-Distances for Anomaly Detection	Dec 2, 2023	Anomaly DetectionDensity Estimation	—Unverified
BadSAD: Clean-Label Backdoor Attacks against Deep Semi-Supervised Anomaly Detection	Dec 17, 2024	Anomaly DetectionBackdoor Attack	—Unverified
Anomaly Detection for Scalable Task Grouping in Reinforcement Learning-based RAN Optimization	Dec 6, 2023	Anomaly Detection	—Unverified
A General Framework for Unsupervised Anomaly Detection	Jan 1, 2021	Anomaly DetectionOut-of-Distribution Detection	—Unverified
A Deep Learning Approach to Anomaly Sequence Detection for High-Resolution Monitoring of Power Systems	Dec 9, 2020	Anomaly DetectionGenerative Adversarial Network	—Unverified
Back to Bayesics: Uncovering Human Mobility Distributions and Anomalies with an Integrated Statistical and Neural Framework	Oct 1, 2024	Anomaly Detection	—Unverified

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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