Robust Learning with Adversarial Perturbations and Label Noise: A Two-Pronged Defense Approach

Despite great success achieved, deep learning methods are vulnerable to noise in the training dataset, including adversarial perturbations and annotation noise. These harmful factors significantly influence the learning process of deep models, leading to less confident models. However, existing methods have not yet studied this practical and challenging issue. In this paper, we propose a novel robust learning method, i.e., Two-Pronged Defense (TPD), which is capable of eliminating negative effects of both data perturbations and label noise during the learning process. On the one hand, to defend against delusive adversarial examples, the proposed method designs an asymmetric adversarial contrastive learning strategy to craft worse-case noisy example for original training data, and train the model to align the semantic between the perturbed data and the original data. In light of this, the TPD would be able to improve the generalization ability of the model on the potential adversarial examples. On the other hand, to combat noisy labels, the TPD applies semi-supervised learning by identifying and discarding noisy labels via a novel designed identification method. Extensive experiments on benchmarks demonstrate the incapability of existing methods and the effectiveness of the proposed method when facing both data and label noise. This work is the very first attempt in learning with data and label noise, and we hope it can pave the way for future studies in related fields.