  A recently-proposed technique called self-adaptive training augments modern
neural networks by allowing them to adjust training labels on the fly, to avoid
overfitting to samples that may be mislabeled or otherwise non-representative.
By combining the self-adaptive objective with mixup, we further improve the
accuracy of self-adaptive models for image recognition; the resulting
classifier obtains state-of-the-art accuracies on datasets corrupted with label
noise. Robustness to label noise implies a lower generalization gap; thus, our
approach also leads to improved generalizability. We find evidence that the
Rademacher complexity of these algorithms is low, suggesting a new path towards
provable generalization for this type of deep learning model. Last, we
highlight a novel connection between difficulties accounting for rare classes
and robustness under noise, as rare classes are in a sense indistinguishable
from label noise. Our code can be found at
https://github.com/Tuxianeer/generalizationconfusion.
