AUROC: 98.4
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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
논문
AUC: 98.7%FPS: 24
AUC: 0.972
AUC: 78.3%
AUC: 0.939
AUC: 99.7%
AUC: 0.907
AUC: 97.9%
AUC: 98.2%
AUC: 99.7%
AUC: 67.12%
AUC: 0.813
AUC: 0.923
AUC: 0.937
AUC: 71.63
AUC: 98.9%
Segmentation AP: 57.8Segmentation AUPRO: 96.9Segmentation AUROC: 99.1Detection AUROC: 88.5
AUC: 97.84
UniNet: A Contrastive Learning-guided Unified Framework with Feature Selection for Anomaly Detection
Segmentation AUPRO: 95.55Detection AUROC: 95.76