Dense Classification and Implanting for Few-Shot Learning

Yann Lifchitz; Yannis Avrithis; Sylvaine Picard; Andrei Bursuc

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arXiv · 1903.05050

Dense Classification and Implanting for Few-Shot Learning

Yann Lifchitz·Yannis Avrithis·Sylvaine Picard·Andrei Bursuc

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Abstract

Training deep neural networks from few examples is a highly challenging and key problem for many computer vision tasks. In this context, we are targeting knowledge transfer from a set with abundant data to other sets with few available examples. We propose two simple and effective solutions: (i) dense classification over feature maps, which for the first time studies local activations in the domain of few-shot learning, and (ii) implanting, that is, attaching new neurons to a previously trained network to learn new, task-specific features. On miniImageNet, we improve the prior state-of-the-art on few-shot classification, i.e., we achieve 62.5%, 79.8% and 83.8% on 5-way 1-shot, 5-shot and 10-shot settings respectively.

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Yann Lifchitz, Yannis Avrithis, Sylvaine Picard, Andrei Bursuc. 2019-03-12. Dense Classification and Implanting for Few-Shot Learning. https://arxiv.org/abs/1903.05050

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