  Meta-learning (ML) has emerged as a promising direction in learning models
under constrained resource settings like few-shot learning. The popular
approaches for ML either learn a generalizable initial model or a generic
parametric optimizer through episodic training. The former approaches leverage
the knowledge from a batch of tasks to learn an optimal prior. In this work, we
study the importance of a batch for ML. Specifically, we first incorporate a
batch episodic training regimen to improve the learning of the generic
parametric optimizer. We also hypothesize that the common assumption in batch
episodic training that each task in a batch has an equal contribution to
learning an optimal meta-model need not be true. We propose to weight the tasks
in a batch according to their "importance" in improving the meta-model's
learning. To this end, we introduce a training curriculum motivated by
selective focus in humans, called task attended meta-training, to weight the
tasks in a batch. Task attention is a standalone module that can be integrated
with any batch episodic training regimen. The comparisons of the models with
their non-task-attended counterparts on complex datasets like miniImageNet and
tieredImageNet validate its effectiveness.
