Semi-Supervised and Active Few-Shot Learning with Prototypical Networks

Rinu Boney; Alexander Ilin

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arXiv · 1711.10856

Semi-Supervised and Active Few-Shot Learning with Prototypical Networks

Rinu Boney·Alexander Ilin

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Abstract

We consider the problem of semi-supervised few-shot classification where a classifier needs to adapt to new tasks using a few labeled examples and (potentially many) unlabeled examples. We propose a clustering approach to the problem. The features extracted with Prototypical Networks are clustered using $K$-means with the few labeled examples guiding the clustering process. We note that in many real-world applications the adaptation performance can be significantly improved by requesting the few labels through user feedback. We demonstrate good performance of the active adaptation strategy using image data.

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Rinu Boney, Alexander Ilin. 2018-04-25. Semi-Supervised and Active Few-Shot Learning with Prototypical Networks. https://arxiv.org/abs/1711.10856

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