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 and standard 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：对抗性对比学习（ACL）无需昂贵的数据标注，却能输出鲁棒表示，既能抵御对抗攻击，又能泛化至广泛的下游任务。然而，ACL需要大量运行时间生成所有训练数据的对抗性变体，这限制了其在大规模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，通过搜索能最小化表示性散度（即自然数据与其虚拟对抗变体间表示距离）的信息性子集。通过遍历所有可能子集的原始RCS解在计算上不可行，因此我们从理论上将RCS转化为子模最大化的替代问题，其贪心搜索是高效解，且对原问题具有最优性保证。实验表明，我们的综合结果证实RCS能在显著降低鲁棒性和标准可迁移性损失的前提下大幅加速ACL。值得注意的是，据我们所知，我们是首个通过RCS在大规模ImageNet-1K数据集上高效执行ACL并获得有效鲁棒表示的工作。