Towards Efficient Gradient-Based Meta-Learning in Heterogenous Environments

Thomas Goerttler, Luis Müller, Klaus Obermayer

Keywords: few-shot learning, heterogeneous datasets, cross-domain adaptation

TL;DR: In our paper, we propose a nonparametric version of MAML which is able to solve problems in heterogeneous enviroments

Abstract: A challenging problem for machine learning is few-shot learning, as its models usually require many training samples. Since meta-learning models have strong fine-tuning capabilities for the distribution of tasks, many of them have been applied to few-shot learning. Model-agnostic meta-learning (MAML) is one of the most popular ones. Recent studies showed that MAML-trained models tend to reuse learned features and do not perform strong adaption, especially in the earlier layers. This paper presents an in-detail analysis of this phenomenon by analyzing MAML's components of different variants. Our results show an interesting relationship between the importance of fine-tuning earlier layers and the difference in the distribution between training and testing. As a result, we determine a fundamental weakness of existing MAML variants when the task distribution is heterogeneous, e.g., the numbers of classes do not match during testing and training. We propose a novel nonparametric version of MAML that overcomes these issues while still being able to perform cross-domain adaption.

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