  MixUp is an effective data augmentation method to regularize deep neural
networks via random linear interpolations between pairs of samples and their
labels. It plays an important role in model regularization, semi-supervised
learning and domain adaption. However, despite its empirical success, its
deficiency of randomly mixing samples has poorly been studied. Since deep
networks are capable of memorizing the entire dataset, the corrupted samples
generated by vanilla MixUp with a badly chosen interpolation policy will
degrade the performance of networks. To overcome the underfitting by corrupted
samples, inspired by Meta-learning (learning to learn), we propose a novel
technique of learning to mixup in this work, namely, MetaMixUp. Unlike the
vanilla MixUp that samples interpolation policy from a predefined distribution,
this paper introduces a meta-learning based online optimization approach to
dynamically learn the interpolation policy in a data-adaptive way. The
validation set performance via meta-learning captures the underfitting issue,
which provides more information to refine interpolation policy. Furthermore, we
adapt our method for pseudo-label based semisupervised learning (SSL) along
with a refined pseudo-labeling strategy. In our experiments, our method
achieves better performance than vanilla MixUp and its variants under
supervised learning configuration. In particular, extensive experiments show
that our MetaMixUp adapted SSL greatly outperforms MixUp and many
state-of-the-art methods on CIFAR-10 and SVHN benchmarks under SSL
configuration.
