  Modern meta-learning approaches for image classification rely on increasingly
deep networks to achieve state-of-the-art performance, making batch
normalization an essential component of meta-learning pipelines. However, the
hierarchical nature of the meta-learning setting presents several challenges
that can render conventional batch normalization ineffective, giving rise to
the need to rethink normalization in this setting. We evaluate a range of
approaches to batch normalization for meta-learning scenarios, and develop a
novel approach that we call TaskNorm. Experiments on fourteen datasets
demonstrate that the choice of batch normalization has a dramatic effect on
both classification accuracy and training time for both gradient based and
gradient-free meta-learning approaches. Importantly, TaskNorm is found to
consistently improve performance. Finally, we provide a set of best practices
for normalization that will allow fair comparison of meta-learning algorithms.
