What is Contrastive learning, data augmentation?

Data augmentation makes new training inputs by transformations that should not change what matters about them (noise, masking, small shifts). Contrastive learning trains an encoder so that two augmented views of the same example have similar codes and views of different examples dissimilar ones; with the InfoNCE loss each view must pick its partner among the $2m - 1$ other views of a batch of $m$ examples, through a softmax of cosine similarities divided by a temperature (van den Oord et al., 2018; Chen et al., 2020).