Meta-Curvature

We propose to learn curvature information for better generalization and fast model adaptation, called meta-curvature. Based on the model-agnostic meta-learner (MAML), we learn to transform the gradients in the inner optimization such that the transformed gradients achieve better generalization performance to a new task. For training large scale neural networks, we decompose the curvature matrix into smaller matrices and capture the dependencies of the model's parameters with a series of tensor products. We demonstrate the effects of our proposed method on both few-shot image classification and few-shot reinforcement learning tasks. Experimental results show consistent improvements on classification tasks and promising results on reinforcement learning tasks. Furthermore, we observe faster convergence rates of the meta-training process. Finally, we present an analysis that explains better generalization performance with the meta-trained curvature.