Although pre-trained models such as Contrastive Language-Image Pre-Training (CLIP)
show impressive generalization results, their robustness is still limited under Out-of …

Abstract Out-of-Distribution (OOD) Generalization aims to learn robust models that
generalize well to various environments without fitting to distribution-specific features …

Overfitting negatively impacts the generalization ability of deep neural networks (DNNs) in
both natural and adversarial training. Existing methods struggle to consistently address …

We present a novel method for fabricating customizable pressure sensors by tuning the infill
characteristics of flexible 3-D prints, addressing the demand for precise sensing solutions …

Adversarial training (AT) refers to integrating adversarial examples--inputs altered with
imperceptible perturbations that can significantly impact model predictions--into the training …

Catastrophic overfitting (CO) presents a significant challenge in single-step adversarial
training (AT), manifesting as highly distorted deep neural networks (DNNs) that are …

Adversarial training is a simple and effective approach to defend against adversarial attacks.
However, most adversarial training methods face expensive time and computational costs …

Deep neural networks (DNNs) have achieved remarkable success across various fields but
remain highly vulnerable to adversarial attacks, prompting the development of numerous …

Despite the remarkable achievements of Deep Neural Networks (DNNs) in handling diverse
tasks, these high-performing models remain susceptible to adversarial attacks …

The widespread adoption of deep neural networks (DNNs) has raised significant concerns
about their robustness, particularly in real-world environments characterized by inherent …