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Out Of Distribution Detection On Cifar 10 Vs

評価指標

AUROC
FPR95

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

モデル名
AUROC
FPR95
Paper TitleRepository
ZODE-KNN97.1218.29Boosting Out-of-Distribution Detection with Multiple Pre-trained Models-
R+ViT finetuned on CIFAR-1098.52-Exploring the Limits of Out-of-Distribution Detection-
ViT finetuned on CIFAR-1098.42-Exploring the Limits of Out-of-Distribution Detection-
DHM100-Deep Hybrid Models for Out-of-Distribution Detection-
WRN 40-2 + Rotation Prediction90.9-Using Self-Supervised Learning Can Improve Model Robustness and Uncertainty-
MLP-Mixer finetuned on CIFAR-1097.85-Exploring the Limits of Out-of-Distribution Detection-
Forte97.63 ± 00.15-Forte : Finding Outliers with Representation Typicality Estimation-
ERD (ResNet18)95.1-Semi-supervised novelty detection using ensembles with regularized disagreement-
WRN 40-2 (MSP Baseline)87.9-A Baseline for Detecting Misclassified and Out-of-Distribution Examples in Neural Networks-
Wide 40-2 + OECC94.9-Outlier Exposure with Confidence Control for Out-of-Distribution Detection-
OpenHybrid95.1-Hybrid Models for Open Set Recognition-
Isolation Forest on EfficientNet Softmax values91.95-Out-of-Distribution Detection Using Outlier Detection Methods-
ResNet + diagonal elements of Gram matrix79.7-Detecting Out-of-Distribution Examples with In-distribution Examples and Gram Matrices-
WRN 40-2 + OE93.3-Deep Anomaly Detection with Outlier Exposure-
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Out Of Distribution Detection On Cifar 10 Vs | SOTA | HyperAI超神経