Adversarially robust knowledge distillation aims to compress large-scale models into
lightweight models while preserving adversarial robustness and natural performance on a …

Few-shot learning (FSL) facilitates a variety of computer vision tasks yet remains vulnerable
to adversarial attacks. Existing adversarially robust FSL methods rely on either visual …

Adversarial robustness generally relies on large-scale architectures and datasets, hindering
resource-efficient deployment. For scalable solutions, adversarially robust knowledge …

Few-shot image classification (FSIC) is beneficial for a variety of real-world scenarios,
aiming to construct a recognition system with limited training data. In this article, we extend …

Despite remarkable achievements in deep learning across various domains, its inherent
vulnerability to adversarial examples still remains a critical concern for practical deployment …

We introduce the $ ARMOR_D $ methods as novel approaches to enhancing the
adversarial robustness of deep learning models. These methods are based on a new class …

Benefiting from the robust feature representation capability of convolutional neural networks
(CNNs), the object detection technology of intelligent high-altitude UAV remote sensing has …

This research addresses the critical challenge of classifying surface defects in lithium
electronic components, crucial for ensuring the reliability and safety of lithium batteries. With …

A well-established study of adversarial training has been proposed to improve network
robustness against adversarial examples in the context of deep learning. However, its …