Recentstudieshaveshownthat the transferability of adversarial examples exists for CNNs, and the same holds true for ViTs.

Unsupervised domain adaptation has attracted appealing academic attentions by transferring knowledge from labeled source domain to unlabeled target domain.

However, much less effort has been devoted to exploring the adversarial robustness of pre-trained models.

Diffusion models have significantly advanced the field of generative modeling.

To speed up ACL, this paper proposes a robustness-aware coreset selection (RCS) method.

Specifically, we randomly sample an example and adopt a first-order procedure to approximate the Hessian/vector product, which makes computing more efficient by interpolating two neighboring gradients.