Efficient base class selection algorithms for few-shot classification

Abstract

Few-shot classification is a task to learn a classifier for novel classes with a limited number of examples on top of the known base classes which have a sufficient number of examples. In recent years, significant progress has been achieved on this task. However, despite the importance of selecting the base classes themselves for better knowledge transfer, few works have paid attention to this point. In this paper, we propose two types of base class selection algorithms that are suitable for few-shot classification tasks. One is based on the thesaurus-tree structure of class names, and the other is based on word embeddings. In our experiments using representative few-shot learning methods on the ILSVRC dataset, we show that these two algorithms can significantly improve the performance compared to a naive class selection method. Moreover, they do not require high computational and memory costs, which is an important advantage to scale to a very large number of base classes.