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.

翻译：现有的大多数小样本学习方法在元训练阶段需要大量标注数据，这成为其主要的应用瓶颈。为减少对标签的依赖，针对小样本学习提出了半监督元训练（SSMT）设定：基类中仅包含少量标注样本和大量未标注样本。然而，该设定下的现有方法需要从未标注集中进行类别感知的样本选择，这与未标注集的实际假设相矛盾。本文提出一种实用化的半监督元训练设定，使用真正未标注数据以促进小样本学习在现实场景中的应用。为更高效地利用标注数据与真实未标注数据，我们设计了一种简单有效的元训练框架——基于伪标签的元学习（PLML）。首先，通过常规半监督学习方法训练分类器，并利用该分类器为未标注数据生成伪标签；随后基于标注数据与伪标签数据构建小样本任务，并设计一种融合特征平滑与噪声抑制的新型微调方法，以更好地从含噪声标签中学习小样本模型。令人意外的是，在两个小样本学习数据集上的大量实验表明：该简单元训练框架不仅有效阻止了有限标注数据下各类小样本模型的性能衰减，还显著超越了现有最优的半监督元训练模型。此外，受益于元训练机制，我们的方法同时提升了两种代表性半监督学习算法的性能。