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 3051–3060 of 4856 papers

Title	Date	Tasks	Status
Self-Supervised Masked Mesh Learning for Unsupervised Anomaly Detection on 3D Cortical Surfaces	Dec 7, 2024	Anomaly DetectionUnsupervised Anomaly Detection	—Unverified
Self-Supervised Masking for Unsupervised Anomaly Detection and Localization	May 13, 2022	Anomaly DetectionAnomaly Localization	—Unverified
Self-supervised Normality Learning and Divergence Vector-guided Model Merging for Zero-shot Congenital Heart Disease Detection in Fetal Ultrasound Videos	Mar 10, 2025	Anomaly DetectionPrivacy Preserving	—Unverified
Self-Supervised Out-of-Distribution Detection in Brain CT Scans	Nov 10, 2020	Anomaly DetectionOut-of-Distribution Detection	—Unverified
Self-Supervised-RCNN for Medical Image Segmentation with Limited Data Annotation	Jul 17, 2022	Anomaly DetectionImage Segmentation	—Unverified
Self-Supervised Representation Learning for Visual Anomaly Detection	Jun 17, 2020	Anomaly DetectionGeneral Classification	—Unverified
Self-Supervised Representation Learning via Neighborhood-Relational Encoding	Aug 27, 2019	Anomaly DetectionDecoder	—Unverified
Self-Supervised Texture Image Anomaly Detection By Fusing Normalizing Flow and Dictionary Learning	Sep 15, 2022	Anomaly DetectionDictionary Learning	—Unverified
Self-Supervised Time-Series Anomaly Detection Using Learnable Data Augmentation	Jun 18, 2024	Anomaly DetectionContrastive Learning	—Unverified
Self-Supervised Training with Autoencoders for Visual Anomaly Detection	Jun 23, 2022	Anomaly DetectionDenoising	—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