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 3431–3440 of 4856 papers

Title	Date	Tasks	Status
Unsupervised 3D Brain Anomaly Detection	Oct 9, 2020	Anomaly Detection	—Unverified
Unsupervised Abnormality Detection through Mixed Structure Regularization (MSR) in Deep Sparse Autoencoders	Feb 28, 2019	Anomaly DetectionDenoising	—Unverified
Unsupervised Abnormality Detection Using Heterogeneous Autonomous Systems	Jun 5, 2020	Anomaly DetectionAutonomous Vehicles	—Unverified
Unsupervised Abnormal Traffic Detection through Topological Flow Analysis	May 14, 2022	Anomaly DetectionUnsupervised Anomaly Detection	—Unverified
Unsupervised Adversarial Anomaly Detection using One-Class Support Vector Machines	Jan 1, 2018	Anomaly DetectionBIG-bench Machine Learning	—Unverified
Unsupervised and Semi-supervised Anomaly Detection with LSTM Neural Networks	Oct 25, 2017	Anomaly DetectionSemi-supervised Anomaly Detection	—Unverified
Unsupervised Anomalous Data Space Specification	Oct 18, 2018	Anomaly Detection	—Unverified
Unsupervised Anomalous Trajectory Detection for Crowded Scenes	Jul 3, 2019	Anomaly DetectionClustering	—Unverified
Unsupervised Anomaly and Change Detection with Multivariate Gaussianization	Apr 12, 2022	Anomaly DetectionChange Detection	—Unverified
Unsupervised Anomaly Detection and Localization with Generative Adversarial Networks	Sep 5, 2024	Anomaly DetectionUnsupervised 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