In adversarial machine learning, deep neural networks can fit the adversarial examples on
the training dataset but have poor generalization ability on the test set. This phenomenon is …

Abstract Out-of-distribution (OOD) generalization has attracted increasing research attention
in recent years, due to its promising experimental results in real-world applications …

Deep neural networks (DNNs) are vulnerable to adversarial attacks. It is found empirically
that adversarially robust generalization is crucial in establishing defense algorithms against …

Deep neural networks (DNNs) are shown to be vulnerable to adversarial examples. A well-
trained model can be easily attacked by adding small perturbations to the original data. One …

In this work, we explore Hypothesis Transfer Learning (HTL) under adversarial attacks. In
this setting, a learner has access to a training dataset of size $ n $ from an underlying …

Adversarial training has emerged as a popular approach for training models that are robust
to inference-time adversarial attacks. However, our theoretical understanding of why and …

In adversarial machine learning, neural networks suffer from a significant issue known as
robust overfitting, where the robust test accuracy decreases over epochs (Rice et al., 2020) …

Despite the growing prevalence of artificial neural networks in real-world applications, their
vulnerability to adversarial attacks remains a significant concern, which motivates us to …

Multi-channel learning has gained significant attention in recent applications, where neural
networks with t-product layers (t-NNs) have shown promising performance through novel …

Abstract Training Deep Neural Networks (DNNs) with adversarial examples often results in
poor generalization to test-time adversarial data. This paper investigates this issue, known …