pokaxpoka / deep_Mahalanobis_detector

Code for the paper "A Simple Unified Framework for Detecting Out-of-Distribution Samples and Adversarial Attacks".
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Baselines comparison #15

Open XTxiatong opened 2 years ago

XTxiatong commented 2 years ago

You merged the in-distribution and out-of-distribution test set and split out new train/val/test set for LR based on Mahalanobis score. However, you don't do it in the same way for ODIN and temperature scaling. Is that fair? At least, I suppose you can use the same subset to report and compare AUC.