  Most existing few-shot learning (FSL) methods require a large amount of
labeled data in meta-training, which is a major limit. To reduce the
requirement of labels, a semi-supervised meta-training (SSMT) setting has been
proposed for FSL, which includes only a few labeled samples and numbers of
unlabeled samples in base classes. However, existing methods under this setting
require class-aware sample selection from the unlabeled set, which violates the
assumption of unlabeled set. In this paper, we propose a practical
semi-supervised meta-training setting with truly unlabeled data to facilitate
the applications of FSL in realistic scenarios. To better utilize both the
labeled and truly unlabeled data, we propose a simple and effective
meta-training framework, called pseudo-labeling based meta-learning (PLML).
Firstly, we train a classifier via common semi-supervised learning (SSL) and
use it to obtain the pseudo-labels of unlabeled data. Then we build few-shot
tasks from labeled and pseudo-labeled data and design a novel finetuning method
with feature smoothing and noise suppression to better learn the FSL model from
noise labels. Surprisingly, through extensive experiments across two FSL
datasets, we find that this simple meta-training framework effectively prevents
the performance degradation of various FSL models under limited labeled data,
and also significantly outperforms the state-of-the-art SSMT models. Besides,
benefiting from meta-training, our method also improves two representative SSL
algorithms as well.
