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Out Of Distribution Detection On Cifar 100 Vs

Metrics

AUPR
AUROC

Results

Performance results of various models on this benchmark

Paper TitleRepository
DHM100100Deep Hybrid Models for Out-of-Distribution Detection-
Ensemble of ViTs-98.11Exploring the Limits of Out-of-Distribution Detection
ViT-L_16 finetuned on CIFAR-100-97.98Exploring the Limits of Out-of-Distribution Detection
R50+ViT_B-16 finetuned on CIFAR-10092.0896.23Exploring the Limits of Out-of-Distribution Detection
ViT_B-16 finetuned on CIFAR-10091.8995.53Exploring the Limits of Out-of-Distribution Detection
MLP-Mixer_B-16 finetuned on CIFAR-10090.2295.31Exploring the Limits of Out-of-Distribution Detection
CLIP using class name words describing the two distributions-94.68Exploring the Limits of Out-of-Distribution Detection
ERD (ResNet18)-94.3Semi-supervised novelty detection using ensembles with regularized disagreement
OpenHybrid-85.6Hybrid Models for Open Set Recognition-
Maximum Logit Score (MLS)-83.2Open-Set Recognition: a Good Closed-Set Classifier is All You Need?
WRN 40-2 + OECC35.278.7Outlier Exposure with Confidence Control for Out-of-Distribution Detection
Joint contrastive and supervised training-78.3Contrastive Training for Improved Out-of-Distribution Detection-
ResNet + diagonal elements of Gram matrix-76.6Detecting Out-of-Distribution Examples with In-distribution Examples and Gram Matrices
DenseNet + diagonal elements of Gram matrix-70.1Detecting Out-of-Distribution Examples with In-distribution Examples and Gram Matrices
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