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The goal of few-shot learning is to learn a classifier that can recognizeunseen classes from limited support data with labels. A common practice forthis task is to train a model on the base set first and then transfer to novelclasses through fine-tuning (Here fine-tuning procedure is defined astransferring knowledge from base to novel data, i.e. learning to transfer infew-shot scenario.) or meta-learning. However, as the base classes have nooverlap to the novel set, simply transferring whole knowledge from base data isnot an optimal solution since some knowledge in the base model may be biased oreven harmful to the novel class. In this paper, we propose to transfer partialknowledge by freezing or fine-tuning particular layer(s) in the base model.Specifically, layers will be imposed different learning rates if they arechosen to be fine-tuned, to control the extent of preserved transferability. Todetermine which layers to be recast and what values of learning rates for them,we introduce an evolutionary search based method that is efficient tosimultaneously locate the target layers and determine their individual learningrates. We conduct extensive experiments on CUB and mini-ImageNet to demonstratethe effectiveness of our proposed method. It achieves the state-of-the-artperformance on both meta-learning and non-meta based frameworks. Furthermore,we extend our method to the conventional pre-training + fine-tuning paradigmand obtain consistent improvement.

Partial Is Better Than All: Revisiting Fine-tuning Strategy for Few-shot Learning