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 1621–1630 of 4856 papers

Title	Date	Tasks	Status	Score
Early Anomaly Detection in Time Series: A Hierarchical Approach for Predicting Critical Health Episodes	Oct 22, 2020	Anomaly DetectionTime Series	CodeCode Available	5
Rethinking Medical Anomaly Detection in Brain MRI: An Image Quality Assessment Perspective	Aug 15, 2024	Anomaly DetectionImage Quality Assessment	CodeCode Available	5
E-ABIN: an Explainable module for Anomaly detection in BIological Networks	Jun 25, 2025	Anomaly Detection	CodeCode Available	5
Bridging 3D Anomaly Localization and Repair via High-Quality Continuous Geometric Representation	May 30, 2025	3D Anomaly DetectionAnomaly Detection	CodeCode Available	5
Action Sequence Augmentation for Early Graph-based Anomaly Detection	Oct 20, 2020	Anomaly DetectionData Augmentation	CodeCode Available	5
Revisiting Graph Contrastive Learning for Anomaly Detection	May 4, 2023	Anomaly DetectionAttribute	CodeCode Available	5
Effective and Efficient Representation Learning for Flight Trajectories	Dec 21, 2024	Anomaly DetectionRepresentation Learning	CodeCode Available	5
Efficient GAN-Based Anomaly Detection	Feb 17, 2018	Anomaly Detection	CodeCode Available	5
Breast Cancer Detection Using Convolutional Neural Networks	Mar 17, 2020	Anomaly DetectionBreast Cancer Detection	CodeCode Available	5
Adaptive Anomaly Detection in Network Flows with Low-Rank Tensor Decompositions and Deep Unrolling	Sep 17, 2024	Anomaly DetectionDomain Adaptation	CodeCode Available	5

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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