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 4021–4030 of 4856 papers

Title	Date	Tasks	Status
Post-Hoc Calibrated Anomaly Detection	Mar 25, 2025	Anomaly DetectionBinary Classification	—Unverified
Postulating Exoplanetary Habitability via a Novel Anomaly Detection Method	Sep 6, 2021	Anomaly DetectionClustering	—Unverified
Practical Anomaly Detection over Multivariate Monitoring Metrics for Online Services	Aug 19, 2023	Anomaly Detection	—Unverified
Practical Applications of Advanced Cloud Services and Generative AI Systems in Medical Image Analysis	Mar 26, 2024	Anomaly DetectionDiversity	—Unverified
Practical Machine Learning for Cloud Intrusion Detection: Challenges and the Way Forward	Sep 20, 2017	Anomaly DetectionBIG-bench Machine Learning	—Unverified
PR-Attack: Coordinated Prompt-RAG Attacks on Retrieval-Augmented Generation in Large Language Models via Bilevel Optimization	Apr 10, 2025	Anomaly DetectionBilevel Optimization	—Unverified
Precision and Recall for Range-Based Anomaly Detection	Jan 9, 2018	Anomaly Detection	—Unverified
(Predictable) Performance Bias in Unsupervised Anomaly Detection	Sep 25, 2023	Anomaly DetectionFairness	—Unverified
Predicting BVD Re-emergence in Irish Cattle From Highly Imbalanced Herd-Level Data Using Machine Learning Algorithms	Apr 17, 2025	Anomaly DetectionBinary Classification	—Unverified
Predictive Analytics Using Smartphone Sensors for Depressive Episodes	Mar 24, 2016	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