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

• URL: http://arxiv.org/abs/2209.13777v1
• Date: Wed, 28 Sep 2022 02:11:34 GMT
• Title: An Embarrassingly Simple Approach to Semi-Supervised Few-Shot Learning
• Authors: Xiu-Shen Wei and He-Yang Xu and Faen Zhang and Yuxin Peng and Wei Zhou
• Abstract summary: We propose a simple but quite effective approach to predict accurate negative pseudo-labels of unlabeled data from an indirect learning perspective. Our approach can be implemented in just few lines of code by only using off-the-shelf operations.
• Score: 58.59343434538218
• License: http://creativecommons.org/licenses/by/4.0/
• 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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