An Embarrassingly Simple Approach to Semi-Supervised Few-Shot Learning

Xiu-Shen Wei; He-Yang Xu; Faen Zhang; Yuxin Peng; Wei Zhou

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arXiv · 2209.13777

An Embarrassingly Simple Approach to Semi-Supervised Few-Shot Learning

Xiu-Shen Wei·He-Yang Xu·Faen Zhang·Yuxin Peng·Wei Zhou

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Abstract

Semi-supervised few-shot learning consists in training a classifier to adapt to new tasks with limited labeled data and a fixed quantity of unlabeled data. Many sophisticated methods have been developed to address the challenges this problem comprises. In this paper, we propose a simple but quite effective approach to predict accurate negative pseudo-labels of unlabeled data from an indirect learning perspective, and then augment the extremely label-constrained support set in few-shot classification tasks. Our approach can be implemented in just few lines of code by only using off-the-shelf operations, yet it is able to outperform state-of-the-art methods on four benchmark datasets.

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Xiu-Shen Wei, He-Yang Xu, Faen Zhang, Yuxin Peng, Wei Zhou. 2022-09-28. An Embarrassingly Simple Approach to Semi-Supervised Few-Shot Learning. https://arxiv.org/abs/2209.13777

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