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 961–970 of 4856 papers

Title	Date	Tasks	Status
Abnormality Detection in Mammography using Deep Convolutional Neural Networks	Mar 5, 2018	Anomaly Detection	—Unverified
2DSig-Detect: a semi-supervised framework for anomaly detection on image data using 2D-signatures	Sep 8, 2024	Anomaly Detection	—Unverified
Anomaly Detection for Fraud in Cryptocurrency Time Series	Jul 23, 2022	Anomaly DetectionTime Series	—Unverified
Anomaly Detection for a Water Treatment System Using Unsupervised Machine Learning	Sep 15, 2017	Anomaly DetectionBIG-bench Machine Learning	—Unverified
AFSC: Adaptive Fourier Space Compression for Anomaly Detection	Apr 17, 2022	Anomaly DetectionData Augmentation	—Unverified
Automated Real-time Anomaly Detection in Human Trajectories using Sequence to Sequence Networks	Jul 12, 2019	Anomaly DetectionTime Series	—Unverified
An Improved Anomaly Detection Model for Automated Inspection of Power Line Insulators	Nov 14, 2023	Anomaly DetectionFault Detection	—Unverified
Automated Processing of eXplainable Artificial Intelligence Outputs in Deep Learning Models for Fault Diagnostics of Large Infrastructures	Mar 19, 2025	Anomaly DetectionExplainable artificial intelligence	—Unverified
Automated Model Selection for Time-Series Anomaly Detection	Aug 25, 2020	Anomaly Detectionmodel	—Unverified
Anomaly Detection for an E-commerce Pricing System	Feb 25, 2019	Anomaly DetectionModel Selection	—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	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