  Meta-learning has proven to be a powerful paradigm for transferring the
knowledge from previous tasks to facilitate the learning of a novel task.
Current dominant algorithms train a well-generalized model initialization which
is adapted to each task via the support set. The crux lies in optimizing the
generalization capability of the initialization, which is measured by the
performance of the adapted model on the query set of each task. Unfortunately,
this generalization measure, evidenced by empirical results, pushes the
initialization to overfit the meta-training tasks, which significantly impairs
the generalization and adaptation to novel tasks. To address this issue, we
actively augment a meta-training task with "more data" when evaluating the
generalization. Concretely, we propose two task augmentation methods, including
MetaMix and Channel Shuffle. MetaMix linearly combines features and labels of
samples from both the support and query sets. For each class of samples,
Channel Shuffle randomly replaces a subset of their channels with the
corresponding ones from a different class. Theoretical studies show how task
augmentation improves the generalization of meta-learning. Moreover, both
MetaMix and Channel Shuffle outperform state-of-the-art results by a large
margin across many datasets and are compatible with existing meta-learning
algorithms.
