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 3941–3950 of 4856 papers

Title	Date	Tasks	Status
On the Nature and Types of Anomalies: A Review of Deviations in Data	Jul 30, 2020	Anomaly Detection	—Unverified
On the Potential of Large Language Models to Solve Semantics-Aware Process Mining Tasks	Apr 29, 2025	Anomaly DetectionBenchmarking	—Unverified
On the Properties of Kullback-Leibler Divergence Between Multivariate Gaussian Distributions	Feb 10, 2021	Anomaly DetectionSafe Reinforcement Learning	—Unverified
On The Relationship between Visual Anomaly-free and Anomalous Representations	Oct 9, 2024	Anomaly DetectionDomain Adaptation	—Unverified
On the Robustness and Anomaly Detection of Sparse Neural Networks	Jul 9, 2022	Anomaly Detection	—Unverified
On the Runtime-Efficacy Trade-off of Anomaly Detection Techniques for Real-Time Streaming Data	Oct 12, 2017	Anomaly DetectionAstronomy	—Unverified
On the Usage of Generative Models for Network Anomaly Detection in Multivariate Time-Series	Oct 16, 2020	Anomaly DetectionIntrusion Detection	—Unverified
On unsupervised-supervised risk and one-class neural networks	Sep 25, 2019	Anomaly DetectionClustering	—Unverified
Open-Set Graph Anomaly Detection via Normal Structure Regularisation	Nov 12, 2023	Anomaly DetectionGraph Anomaly Detection	—Unverified
Open-Set Multivariate Time-Series Anomaly Detection	Oct 18, 2023	Anomaly DetectionContrastive Learning	—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