Model-Agnostic Meta-Learning for Multimodal Task Distributions

Risto Vuorio, Shao-Hua Sun, Hexiang Hu, Joseph J. Lim

Abstract: Gradient-based meta-learners such as MAML (Finn et al., 2017) are able to learn a meta-prior from similar tasks to adapt to novel tasks from the same distribution with few gradient updates. One important limitation of such frameworks is that they seek a common initialization shared across the entire task distribution, substantially limiting the diversity of the task distributions that they are able to learn from. In this paper, we augment MAML with the capability to identify tasks sampled from a multimodal task distribution and adapt quickly through gradient updates. Specifically, we propose a multimodal MAML algorithm that is able to modulate its meta-learned prior according to the identified task, allowing faster adaptation. We evaluate the proposed model on a diverse set of problems including regression, few-shot image classification, and reinforcement learning. The results demonstrate the effectiveness of our model in modulating the meta-learned prior in response to the characteristics of tasks sampled from a multimodal distribution.

Keywords: Meta-learning, gradient-based meta-learning, model-based meta-learning

TL;DR: We proposed a meta-learner that generalizes across a multimodal task distribution by identifying the modes of a task distribution and modulating its meta-learned prior parameters accordingly, allowing faster adaptation through gradient updates.