Semi-Supervised Meta-Learning via Self-Training

The goal of meta-learning is to learn a learning procedure to generate a learner from only a handful of labeled datapoints. However, the learning procedure is learned by a meta-learner from an enormous amount of few-shot tasks constructed from a large amount of labeled datapoints. From this point of view, few-shot learning is also depending on huge amount of labeled data. In this paper, we present a simple and efficient method for few-shot classification in a semi-supervised setting where only a small portion of training samples are labeled. We assign these unlabeled data pseudo-labels using a classifier trained with both labeled and unlabeled data. Once the pseudo-label obtained, we can run meta-learning over tasks constructed from labeled and pseudo-labeled data. We evaluate our method on miniImagenet and tieredImagenet benchmarks whose meta-training sets are split into unlabeled portion and labeled portion further in order to adapt to our framework. Our experimental results confirm that our semi-supervised meta-learning approach acquires a considerable performance gain over meta-learning with only labeled data and significantly outperforms previous state-of-the-art semi-supervised meta-learning methods.