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 3191–3200 of 4856 papers

Title	Date	Tasks	Status
"Stones from Other Hills can Polish Jade": Zero-shot Anomaly Image Synthesis via Cross-domain Anomaly Injection	Jan 25, 2025	Anomaly DetectionImage Generation	—Unverified
Street Scene: A new dataset and evaluation protocol for video anomaly detection	Feb 15, 2019	Anomaly DetectionVideo Anomaly Detection	—Unverified
Strengthening Anomaly Awareness	Apr 15, 2025	Anomaly DetectionUnsupervised Anomaly Detection	—Unverified
STRIC: Stacked Residuals of Interpretable Components for Time Series Anomaly Detection	Sep 29, 2021	Anomaly DetectionArticles	—Unverified
Structural Teacher-Student Normality Learning for Multi-Class Anomaly Detection and Localization	Feb 27, 2024	Anomaly DetectionKnowledge Distillation	—Unverified
Structure-based Anomaly Detection and Clustering	May 19, 2025	Anomaly DetectionClustering	—Unverified
STTS-EAD: Improving Spatio-Temporal Learning Based Time Series Prediction via	Jan 14, 2025	Anomaly DetectionMultivariate Time Series Forecasting	—Unverified
Studying the Robustness of Anti-adversarial Federated Learning Models Detecting Cyberattacks in IoT Spectrum Sensors	Jan 31, 2022	Anomaly DetectionBinary Classification	—Unverified
Study of the Impact of the Big Data Era on Accounting and Auditing	Mar 11, 2024	Anomaly Detection	—Unverified
Sub-clusters of Normal Data for Anomaly Detection	Nov 17, 2020	Anomaly DetectionClustering	—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