Asymptotic Midpoint Mixup for Margin Balancing and Moderate Broadening
Hoyong Kim Semi Lee Kangil Kim
In the feature space, the collapse between features invokes critical problems
in representation learning by remaining the features undistinguished.
Interpolation-based augmentation methods such as mixup have shown their
effectiveness in relieving the collapse problem between different classes,
called inter-class collapse. However, intra-class collapse raised in
coarse-to-fine transfer learning has not been discussed in the augmentation
approach. To address them, we propose a better feature augmentation method,
asymptotic midpoint mixup. The method generates augmented features by
interpolation but gradually moves them toward the midpoint of inter-class
feature pairs. As a result, the method induces two effects: 1) balancing the
margin for all classes and 2) only moderately broadening the margin until it
holds maximal confidence. We empirically analyze the collapse effects by
measuring alignment and uniformity with visualizing representations. Then, we
validate the intra-class collapse effects in coarse-to-fine transfer learning
and the inter-class collapse effects in imbalanced learning on long-tailed
datasets. In both tasks, our method shows better performance than other
augmentation methods.