  The ability to learn new concepts with small amounts of data is a crucial
aspect of intelligence that has proven challenging for deep learning methods.
Meta-learning for few-shot learning offers a potential solution to this
problem: by learning to learn across data from many previous tasks, few-shot
learning algorithms can discover the structure among tasks to enable fast
learning of new tasks. However, a critical challenge in few-shot learning is
task ambiguity: even when a powerful prior can be meta-learned from a large
number of prior tasks, a small dataset for a new task can simply be very
ambiguous to acquire a single model for that task. The Bayesian meta-learning
models can naturally resolve this problem by putting a sophisticated prior
distribution and let the posterior well regularized through Bayesian decision
theory. However, currently known Bayesian meta-learning procedures such as
VERSA suffer from the so-called {\it information preference problem}, that is,
the posterior distribution is degenerated to one point and is far from the
exact one. To address this challenge, we design a novel meta-regularization
objective using {\it cyclical annealing schedule} and {\it maximum mean
discrepancy} (MMD) criterion. The cyclical annealing schedule is quite
effective at avoiding such degenerate solutions. This procedure includes a
difficult KL-divergence estimation, but we resolve the issue by employing MMD
instead of KL-divergence. The experimental results show that our approach
substantially outperforms standard meta-learning algorithms.
