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.

通过插值生成增强特征并逐渐将其移动到类间特征对的中点，提出了一种更好的特征增强方法。该方法具有两个效果：1）平衡所有类的间隔；2）在保持最大置信度的同时适度扩大间隔。在视觉上可视化表示来测量对齐和均匀性的折叠效应，验证了粗细粒度转移学习中的类内折叠效应和长尾数据集上的不平衡学习中的类间折叠效应。在这两个任务中，我们的方法表现优于其他增强方法。