Can we improve meta-learning model in few-shot learning by aligning data distributions?

Abstract

Meta-learning becomes a promising way to solve the few-shot learning problem in recent research. This paradigm mainly relies on hierarchical architecture and episodic training to achieve good generalization on the new learning task. However, as we know, support and query sets in each training task in episodic training only contain a few data points. Too limited training data can lead to data discrepancy between the training and test data which further hurts the performance of learning models in classical supervised and semi-supervised learning. Hence, there is an interesting question: Can we also improve the meta-learning model in few-shot learning by aligning the data distributions of support and query sets? In this work, we try to answer this question. Firstly, we give a theoretical analysis of the generalization ability of meta-learning to show that eliminating the data-distribution discrepancy between the support and query sets in each training task can improve the meta-learning model in few-shot learning. Next, we design an experiment to further verify our analysis. The experimental results show that aligning the data distributions is crucial and even can help the learning model trained by the classical batch-based training achieve better performance on unseen few-shot learning tasks. Furthermore, we also propose two simple meta-batch normalization methods following our result which can be integrated into recent meta-learning methods to improve their performance in few-shot learning.