Related papers: Learning to Select Base Classes for Few-shot Classification

Learning to Select Base Classes for Few-shot Classification

URL: http://arxiv.org/abs/2004.00315v1
Date: Wed, 1 Apr 2020 09:55:18 GMT
Title: Learning to Select Base Classes for Few-shot Classification
Authors: Linjun Zhou, Peng Cui, Xu Jia, Shiqiang Yang, Qi Tian
Abstract summary: We use the Similarity Ratio as an indicator for the generalization performance of a few-shot model. We then formulate the base class selection problem as a submodular optimization problem over Similarity Ratio.
Score: 96.92372639495551
License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Abstract: Few-shot learning has attracted intensive research attention in recent years. Many methods have been proposed to generalize a model learned from provided base classes to novel classes, but no previous work studies how to select base classes, or even whether different base classes will result in different generalization performance of the learned model. In this paper, we utilize a simple yet effective measure, the Similarity Ratio, as an indicator for the generalization performance of a few-shot model. We then formulate the base class selection problem as a submodular optimization problem over Similarity Ratio. We further provide theoretical analysis on the optimization lower bound of different optimization methods, which could be used to identify the most appropriate algorithm for different experimental settings. The extensive experiments on ImageNet, Caltech256 and CUB-200-2011 demonstrate that our proposed method is effective in selecting a better base dataset.

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