  We propose an adaptation of the curriculum training framework, applicable to
state-of-the-art meta learning techniques for few-shot classification.
Curriculum-based training popularly attempts to mimic human learning by
progressively increasing the training complexity to enable incremental concept
learning. As the meta-learner's goal is learning how to learn from as few
samples as possible, the exact number of those samples (i.e. the size of the
support set) arises as a natural proxy of a given task's difficulty. We define
a simple yet novel curriculum schedule that begins with a larger support size
and progressively reduces it throughout training to eventually match the
desired shot-size of the test setup. This proposed method boosts the learning
efficiency as well as the generalization capability. Our experiments with the
MAML algorithm on two few-shot image classification tasks show significant
gains with the curriculum training framework. Ablation studies corroborate the
independence of our proposed method from the model architecture as well as the
meta-learning hyperparameters
