Generalized Adaptation for Few-Shot Learning

2019-11-25 · Liang Song, Jinlu Liu, Yongqiang Qin

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%.

📄 PDF Abstract BibTeX arXiv:1911.10807

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Tasks

Few-Shot Image Classification Few-Shot Learning

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