  Imbalanced data pose challenges for deep learning based classification
models. One of the most widely-used approaches for tackling imbalanced data is
re-weighting, where training samples are associated with different weights in
the loss function. Most of existing re-weighting approaches treat the example
weights as the learnable parameter and optimize the weights on the meta set,
entailing expensive bilevel optimization. In this paper, we propose a novel
re-weighting method based on optimal transport (OT) from a distributional point
of view. Specifically, we view the training set as an imbalanced distribution
over its samples, which is transported by OT to a balanced distribution
obtained from the meta set. The weights of the training samples are the
probability mass of the imbalanced distribution and learned by minimizing the
OT distance between the two distributions. Compared with existing methods, our
proposed one disengages the dependence of the weight learning on the concerned
classifier at each iteration. Experiments on image, text and point cloud
datasets demonstrate that our proposed re-weighting method has excellent
performance, achieving state-of-the-art results in many cases and providing a
promising tool for addressing the imbalanced classification issue.
