Anomaly Detection On One Class Imagenet 30
評価指標
AUROC
評価結果
このベンチマークにおける各モデルのパフォーマンス結果
比較表
モデル名 | AUROC |
---|---|
using-self-supervised-learning-can-improve | 77.9 |
using-self-supervised-learning-can-improve | 84.8 |
using-self-supervised-learning-can-improve | 65.3 |
csi-novelty-detection-via-contrastive | 91.6 |
using-self-supervised-learning-can-improve | 85.7 |
explainable-deep-one-class-classification | 91 |
exposing-outlier-exposure-what-can-be-learned | 99.88 |
exposing-outlier-exposure-what-can-be-learned | 99.90 |
using-self-supervised-learning-can-improve | 81.6 |
using-self-supervised-learning-can-improve | 56.1 |
exposing-outlier-exposure-what-can-be-learned | 97.7 |