Provably Learning Diverse Features in Multi-View Data with Midpoint Mixup. (arXiv:2210.13512v1 [cs.LG])

Mixup is a data augmentation technique that relies on training using random
convex combinations of data points and their labels. In recent years, Mixup has
become a standard primitive used in the training of state-of-the-art image
classification models due to its demonstrated benefits over empirical risk
minimization with regards to generalization and robustness. In this work, we
try to explain some of this success from a feature learning perspective. We
focus our attention on classification problems in which each class …

arxiv data features midpoint