  Contrastive learning (CL) is a popular technique for self-supervised learning
(SSL) of visual representations. It uses pairs of augmentations of unlabeled
training examples to define a classification task for pretext learning of a
deep embedding. Despite extensive works in augmentation procedures, prior works
do not address the selection of challenging negative pairs, as images within a
sampled batch are treated independently. This paper addresses the problem, by
introducing a new family of adversarial examples for constrastive learning and
using these examples to define a new adversarial training algorithm for SSL,
denoted as CLAE. When compared to standard CL, the use of adversarial examples
creates more challenging positive pairs and adversarial training produces
harder negative pairs by accounting for all images in a batch during the
optimization. CLAE is compatible with many CL methods in the literature.
Experiments show that it improves the performance of several existing CL
baselines on multiple datasets.
