Imperfect labels are ubiquitous in real-world datasets. Several recent
successful methods for training deep neural networks (DNNs) robust to label
noise have used two primary techniques: filtering samples based on loss during
a warm-up phase to curate an initial set of cleanly labeled samples, and using
the output of a network as a pseudo-label for subsequent loss calculations. In
this paper, we evaluate different augmentation strategies for algorithms
tackling the "learning with noisy labels" problem. We propose and examine
multiple augmentation strategies and evaluate them using synthetic datasets
based on CIFAR-10 and CIFAR-100, as well as on the real-world dataset
Clothing1M. Due to several commonalities in these algorithms, we find that
using one set of augmentations for loss modeling tasks and another set for
learning is the most effective, improving results on the state-of-the-art and
other previous methods. Furthermore, we find that applying augmentation during
the warm-up period can negatively impact the loss convergence behavior of
correctly versus incorrectly labeled samples. We introduce this augmentation
strategy to the state-of-the-art technique and demonstrate that we can improve
performance across all evaluated noise levels. In particular, we improve
accuracy on the CIFAR-10 benchmark at 90% symmetric noise by more than 15% in
absolute accuracy, and we also improve performance on the Clothing1M dataset.
(K. Nishi and Y. Ding contributed equally to this work)

本文研究了应对 “学习有噪音标签” 问题的多种数据增广策略，通过在 CIFAR-10、CIFAR-100 和 Clothing1M 等数据集上的实验，发现在 warm-up 阶段和学习阶段使用不同的增广策略能够最有效地提高 DNNs 的鲁棒性，同时也提出了一种关于样本筛选的增广方法，该方法在对真实噪音数据集进行处理时比其他基线方法取得了更好的结果。