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.
2020 3rd International Conference on Intelligent Autonomous Systems (ICoIAS)