Subspace Learning for Effective Meta-Learning

Meta-learning aims to extract meta-knowledge from historical tasks to accelerate learning on new tasks. Typical meta-learning algorithms like MAML learn a globally-shared meta-model for all tasks. However, when the task environments are complex, task model parameters are diverse and a common meta-model is insufficient to capture all the meta-knowledge. To address this challenge, in this paper, task model parameters are structured into multiple subspaces, and each subspace represents one type of meta-knowledge. We propose an algorithm to learn the meta-parameters (i.e., subspace bases). We theoretically study the generalization properties of the learned subspaces. Experiments on regression and classification metalearning datasets verify the effectiveness of the proposed algorithm.