  While tasks could come with varying the number of instances and classes in
realistic settings, the existing meta-learning approaches for few-shot
classification assume that the number of instances per task and class is fixed.
Due to such restriction, they learn to equally utilize the meta-knowledge
across all the tasks, even when the number of instances per task and class
largely varies. Moreover, they do not consider distributional difference in
unseen tasks, on which the meta-knowledge may have less usefulness depending on
the task relatedness. To overcome these limitations, we propose a novel
meta-learning model that adaptively balances the effect of the meta-learning
and task-specific learning within each task. Through the learning of the
balancing variables, we can decide whether to obtain a solution by relying on
the meta-knowledge or task-specific learning. We formulate this objective into
a Bayesian inference framework and tackle it using variational inference. We
validate our Bayesian Task-Adaptive Meta-Learning (Bayesian TAML) on multiple
realistic task- and class-imbalanced datasets, on which it significantly
outperforms existing meta-learning approaches. Further ablation study confirms
the effectiveness of each balancing component and the Bayesian learning
framework.
