An Embarrassingly Simple Approach to Semi-Supervised Few-Shot Learning
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.
Cite
Text
Wei et al. "An Embarrassingly Simple Approach to Semi-Supervised Few-Shot Learning." Neural Information Processing Systems, 2022.Markdown
[Wei et al. "An Embarrassingly Simple Approach to Semi-Supervised Few-Shot Learning." Neural Information Processing Systems, 2022.](https://mlanthology.org/neurips/2022/wei2022neurips-embarrassingly/)BibTeX
@inproceedings{wei2022neurips-embarrassingly,
title = {{An Embarrassingly Simple Approach to Semi-Supervised Few-Shot Learning}},
author = {Wei, Xiu-Shen and Xu, H.-Y. and Zhang, Faen and Peng, Yuxin and Zhou, Wei},
booktitle = {Neural Information Processing Systems},
year = {2022},
url = {https://mlanthology.org/neurips/2022/wei2022neurips-embarrassingly/}
}