Understanding the Role of Normalization in Contrastive Representation Learning for Improved Out-of-Distribution Detection

Contrastive learning inherently promotes a large norm for the contrastive features of in-distribution samples, creating a separation between in-distribution and out-of-distribution data in the feature space. This property can be leveraged to improve out-of-distribution detection by incorporating out-of-distribution samples into the contrastive learning process.