  Recent advances in meta-learning has led to remarkable performances on
several few-shot learning benchmarks. However, such success often ignores the
similarity between training and testing tasks, resulting in a potential bias
evaluation. We, therefore, propose a generative approach based on a variant of
Latent Dirichlet Allocation to analyse task similarity to optimise and better
understand the performance of meta-learning. We demonstrate that the proposed
method can provide an insightful evaluation for meta-learning algorithms on two
few-shot classification benchmarks that matches common intuition: the more
similar the higher performance. Based on this similarity measure, we propose a
task-selection strategy for meta-learning and show that it can produce more
accurate classification results than methods that randomly select training
tasks.
