Summary

Investigates out-of-distribution (OOD) detection in practice using Cat vs Dog classifier as toy example. Shows OOD samples cluster around center of logit space while in-distribution samples spread towards positive regions. Behavior is consistent across different CNN architectures, DenseNet models, and ViT models.