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 4531–4540 of 4856 papers

Title	Date	Tasks	Status
A Characteristic Function for Shapley-Value-Based Attribution of Anomaly Scores	Apr 9, 2020	Anomaly DetectionSemi-supervised Anomaly Detection	CodeCode Available
Self-supervised learning for classifying paranasal anomalies in the maxillary sinus	Apr 29, 2024	Anomaly DetectionSelf-Supervised Learning	CodeCode Available
Fence GAN: Towards Better Anomaly Detection	Apr 2, 2019	Anomaly ClassificationAnomaly Detection	CodeCode Available
Self-supervised Learning for Clustering of Wireless Spectrum Activity	Sep 22, 2022	Anomaly DetectionClustering	CodeCode Available
Addressing Gap between Training Data and Deployed Environment by On-Device Learning	Mar 2, 2022	Anomaly DetectionPICO	CodeCode Available
Federated Learning with Reservoir State Analysis for Time Series Anomaly Detection	Feb 8, 2025	Anomaly DetectionComputational Efficiency	CodeCode Available
Self-Supervised Learning for Detecting AI-Generated Faces as Anomalies	Jan 4, 2025	Anomaly DetectionBinary Classification	CodeCode Available
Deep generative models for unsupervised delamination detection using guided waves	Aug 10, 2023	Anomaly DetectionStructural Health Monitoring	CodeCode Available
FedCAP: Robust Federated Learning via Customized Aggregation and Personalization	Oct 16, 2024	Anomaly DetectionFederated Learning	CodeCode Available
One Class Splitting Criteria for Random Forests	Nov 7, 2016	Anomaly DetectionBIG-bench Machine Learning	CodeCode Available

Show:10 25 50

← PrevPage 454 of 486Next →

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