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Few-shot classification is a challenging task which aims to formulate theability of humans to learn concepts from limited prior data and has drawnconsiderable attention in machine learning. Recent progress in few-shotclassification has featured meta-learning, in which a parameterized model for alearning algorithm is defined and trained to learn the ability of handlingclassification tasks on extremely large or infinite episodes representingdifferent classification task, each with a small labeled support set and itscorresponding query set. In this work, we advance this few-shot classificationparadigm by formulating it as a supervised classification learning problem. Wefurther propose multi-episode and cross-way training techniques, whichrespectively correspond to the minibatch and pretraining in classificationproblems. Experimental results on a state-of-the-art few-shot classificationmethod (prototypical networks) demonstrate that both the proposed trainingstrategies can highly accelerate the training process without accuracy loss forvarying few-shot classification problems on Omniglot and miniImageNet.

Training few-shot classification via the perspective of minibatch and pretraining