Adversarial contrastive learning (ACL) does not require expensive data annotations but outputs a robust representation that withstands adversarial attacks and also generalizes to a wide range of downstream tasks. However, ACL needs tremendous running time to generate the adversarial variants of all training data, which limits its scalability to large datasets. To speed up ACL, this paper proposes a robustness-aware coreset selection (RCS) method. RCS does not require label information and searches for an informative subset that minimizes a representational divergence, which is the distance of the representation between natural data and their virtual adversarial variants. The vanilla solution of RCS via traversing all possible subsets is computationally prohibitive. Therefore, we theoretically transform RCS into a surrogate problem of submodular maximization, of which the greedy search is an efficient solution with an optimality guarantee for the original problem. Empirically, our comprehensive results corroborate that RCS can speed up ACL by a large margin without significantly hurting the robustness transferability. Notably, to the best of our knowledge, we are the first to conduct ACL efficiently on the large-scale ImageNet-1K dataset to obtain an effective robust representation via RCS.

翻译：对抗对比学习（ACL）无需昂贵的数据标注，即可输出能够抵御对抗攻击且泛化至广泛下游任务的鲁棒表示。然而，ACL需要耗费大量运行时间生成所有训练数据的对抗变体，这限制了其在大型数据集上的可扩展性。为加速ACL，本文提出一种鲁棒性感知核心集选择（RCS）方法。RCS无需标签信息，通过搜索一个能够最小化表征散度的信息性子集——即自然数据与其虚拟对抗变体之间的表征距离——来实现目标。直接遍历所有可能子集的RCS朴素解法在计算上难以实现。因此，我们从理论上将RCS转化为一个子模最大化代理问题，其贪心搜索算法既能高效求解，又对原始问题具有最优性保证。实验结果全面证实，RCS可在不显著损害鲁棒迁移性的前提下大幅加速ACL。值得注意的是，据我们所知，本文首次通过RCS高效地在大规模ImageNet-1K数据集上执行ACL，从而获得有效的鲁棒表示。