  Model-Agnostic Meta-Learning (MAML) and its variants are popular few-shot
classification methods. They train an initializer across a variety of sampled
learning tasks (also known as episodes) such that the initialized model can
adapt quickly to new tasks. However, current MAML-based algorithms have
limitations in forming generalizable decision boundaries. In this paper, we
propose an approach called MetaMix. It generates virtual feature-target pairs
within each episode to regularize the backbone models. MetaMix can be
integrated with any of the MAML-based algorithms and learn the decision
boundaries generalizing better to new tasks. Experiments on the mini-ImageNet,
CUB, and FC100 datasets show that MetaMix improves the performance of
MAML-based algorithms and achieves state-of-the-art result when integrated with
Meta-Transfer Learning.
