The emergence of Deep Neural Networks (DNNs) has revolutionized various domains,
enabling the resolution of complex tasks spanning image recognition, natural language …

Adversarial contrastive learning (ACL) does not require expensive data annotations but
outputs a robust representation that withstands adversarial attacks and also generalizes to a …

Adversarial examples for deep neural networks (DNNs) are transferable: examples that
successfully fool one white-box surrogate model can also deceive other black-box models …

Adversarial examples (AEs) with small adversarial perturbations can mislead deep neural
networks (DNNs) into wrong predictions. The AEs created on one DNN can also fool another …

Foundation models pre-trained on web-scale vision-language data, such as CLIP, are
widely used as cornerstones of powerful machine learning systems. While pre-training offers …

Software cross-project defect prediction (CPDP) makes use of cross-project (CP) data to
overcome the lack of data necessary to train well-performing software defect prediction …

The performance of Transfer Learning (TL) heavily relies on effective pretraining, which
demands large datasets and substantial computational resources. As a result, executing TL …

Adversarial transferability presents an intriguing phenomenon, where adversarial examples
designed for one model can effectively deceive other models. By exploiting this property …

We study adversarially robust transfer learning, wherein, given labeled data on multiple
(source) tasks, the goal is to train a model with small robust error on a previously unseen …