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 2481–2490 of 4856 papers

Title	Date	Tasks	Status
A review on outlier/anomaly detection in time series data	Feb 11, 2020	Anomaly DetectionOutlier Detection	—Unverified
Are vision language models robust to uncertain inputs?	May 17, 2025	Anomaly DetectionMorphology classification	—Unverified
Argos: Agentic Time-Series Anomaly Detection with Autonomous Rule Generation via Large Language Models	Jan 24, 2025	Anomaly DetectionTime Series	—Unverified
Identifying Backdoor Attacks in Federated Learning via Anomaly Detection	Feb 9, 2022	Anomaly DetectionFederated Learning	—Unverified
A Roadmap Towards Resilient Internet of Things for Cyber-Physical Systems	Oct 16, 2018	Anomaly DetectionAutonomous Vehicles	—Unverified
A Robust and Efficient Multi-Scale Seasonal-Trend Decomposition	Sep 18, 2021	Anomaly DetectionTime Series	—Unverified
A Robust and Explainable Data-Driven Anomaly Detection Approach For Power Electronics	Sep 23, 2022	Anomaly DetectionTime Series	—Unverified
A Robust Autoencoder Ensemble-Based Approach for Anomaly Detection in Text	May 16, 2024	Anomaly DetectionBenchmarking	—Unverified
A Robust Likelihood Model for Novelty Detection	Jun 6, 2023	Adversarial AttackAnomaly Detection	—Unverified
Arrays of (locality-sensitive) Count Estimators (ACE): High-Speed Anomaly Detection via Cache Lookups	Jun 20, 2017	Anomaly DetectionUnsupervised 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	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