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arXiv · 1911.10807

Generalized Adaptation for Few-Shot Learning

Liang Song·Jinlu Liu·Yongqiang Qin
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Abstract

Many Few-Shot Learning research works have two stages: pre-training base model and adapting to novel model. In this paper, we propose to use closed-form base learner, which constrains the adapting stage with pre-trained base model to get better generalized novel model. Following theoretical analysis proves its rationality as well as indication of how to train a well-generalized base model. We then conduct experiments on four benchmarks and achieve state-of-the-art performance in all cases. Notably, we achieve the accuracy of 87.75% on 5-shot miniImageNet which approximately outperforms existing methods by 10%.

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BibTeXRIS

Liang Song, Jinlu Liu, Yongqiang Qin. 2020-06-02. Generalized Adaptation for Few-Shot Learning. https://arxiv.org/abs/1911.10807

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