  Deep image classifiers often perform poorly when training data are heavily
class-imbalanced. In this work, we propose a new regularization technique,
Remix, that relaxes Mixup's formulation and enables the mixing factors of
features and labels to be disentangled. Specifically, when mixing two samples,
while features are mixed in the same fashion as Mixup, Remix assigns the label
in favor of the minority class by providing a disproportionately higher weight
to the minority class. By doing so, the classifier learns to push the decision
boundaries towards the majority classes and balance the generalization error
between majority and minority classes. We have studied the state-of-the art
regularization techniques such as Mixup, Manifold Mixup and CutMix under
class-imbalanced regime, and shown that the proposed Remix significantly
outperforms these state-of-the-arts and several re-weighting and re-sampling
techniques, on the imbalanced datasets constructed by CIFAR-10, CIFAR-100, and
CINIC-10. We have also evaluated Remix on a real-world large-scale imbalanced
dataset, iNaturalist 2018. The experimental results confirmed that Remix
provides consistent and significant improvements over the previous methods.
