Contrastive learning has gained popularity as an effective self-supervised
representation learning technique. Several research directions improve
traditional contrastive approaches, e.g., prototypical contrastive methods
better capture the semantic similarity among instances and reduce the
computational burden by considering cluster prototypes or cluster assignments,
while adversarial instance-wise contrastive methods improve robustness against
a variety of attacks. To the best of our knowledge, no prior work jointly
considers robustness, cluster-wise semantic similarity and computational
efficiency. In this work, we propose SwARo, an adversarial contrastive
framework that incorporates cluster assignment permutations to generate
representative adversarial samples. We evaluate SwARo on multiple benchmark
datasets and against various white-box and black-box attacks, obtaining
consistent improvements over state-of-the-art baselines.

本文提出一种名为 SwARo 的对抗性对比学习框架，结合聚类分配和实例感知对抗样本生成以提高语义相似性和计算效率，并在多个基准数据集上进行评估，获得了与最先进的基线相比的一致改进。