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

• URL: http://arxiv.org/abs/2004.05910v1
• Date: Fri, 10 Apr 2020 03:14:48 GMT
• Title: Training few-shot classification via the perspective of minibatch and pretraining
• Authors: Meiyu Huang, Xueshuang Xiang, Yao Xu
• Abstract summary: Few-shot classification is a challenging task which aims to formulate the ability of humans to learn concepts from limited prior data. Recent progress in few-shot classification has featured meta-learning. We propose multi-episode and cross-way training techniques, which respectively correspond to the minibatch and pretraining in classification problems.
• Score: 10.007569291231915
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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