Remix: Rebalanced Mixup
This addresses the issue of class imbalance in image classification, which is a common problem in real-world datasets, though it is an incremental improvement over existing mixup-based methods.
The paper tackles the problem of poor performance of deep image classifiers when training data is class-imbalanced by proposing Remix, a regularization technique that disentangles mixing factors for features and labels to favor minority classes, resulting in significant improvements over state-of-the-art methods on datasets like CIFAR-10, CIFAR-100, CINIC-10, and iNaturalist 2018.
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.