  Few-shot classification is a challenging task which aims to formulate the
ability of humans to learn concepts from limited prior data and has drawn
considerable attention in machine learning. Recent progress in few-shot
classification has featured meta-learning, in which a parameterized model for a
learning algorithm is defined and trained to learn the ability of handling
classification tasks on extremely large or infinite episodes representing
different classification task, each with a small labeled support set and its
corresponding query set. In this work, we advance this few-shot classification
paradigm by formulating it as a supervised classification learning problem. We
further propose multi-episode and cross-way training techniques, which
respectively correspond to the minibatch and pretraining in classification
problems. Experimental results on a state-of-the-art few-shot classification
method (prototypical networks) demonstrate that both the proposed training
strategies can highly accelerate the training process without accuracy loss for
varying few-shot classification problems on Omniglot and miniImageNet.
