  Modern deep neural networks (DNNs) become frail when the datasets contain
noisy (incorrect) class labels. Many robust techniques have emerged via loss
adjustment, robust loss function, and clean sample selection to mitigate this
issue using the whole dataset. Here, we empirically observe that the dataset
which contains only clean instances in original noisy datasets leads to better
optima than the original dataset even with fewer data. Based on these results,
we state the winning ticket hypothesis: regardless of robust methods, any DNNs
reach the best performance when trained on the dataset possessing only clean
samples from the original (winning ticket). We propose two simple yet effective
strategies to identify winning tickets by looking at the loss landscape and
latent features in DNNs. We conduct numerical experiments by collaborating the
two proposed methods purifying data and existing robust methods for CIFAR-10
and CIFAR-100. The results support that our framework consistently and
remarkably improves performance.
