Cihang Xie

Mitigating Adversarial Effects Through Randomization

2017 · arXiv (Cornell University)

Convolutional neural networks have demonstrated high accuracy on various tasks in recent years. However, they are extremely vulnerable to adversarial examples. For example, imperceptible perturbations added to clean images can cause convolutional neural networks to …

Intriguing properties of adversarial training at scale

2019 · arXiv (Cornell University)

Adversarial training is one of the main defenses against adversarial attacks. In this paper, we provide the first rigorous study on diagnosing elements of adversarial training, which reveals two intriguing properties. First, we study the …

L2B: Learning to Bootstrap Robust Models for Combating Label Noise

2024

Deep neural networks have shown great success in representation learning. However, when learning with noisy labels (LNL), they can easily overfit and fail to generalize to new data. This paper introduces a simple and effective …