The field of meta-learning, or learning-to-learn, has seen a dramatic rise in
interest in recent years. Contrary to conventional approaches to AI where tasks
are solved from scratch using a fixed learning algorithm, meta-learning aims to
improve the learning algorithm itself, given the experience of multiple
learning episodes. This paradigm provides an opportunity to tackle many
conventional challenges of deep learning, including data and computation
bottlenecks, as well as generalization. This survey describes the contemporary
meta-learning landscape. We first discuss definitions of meta-learning and
position it with respect to related fields, such as transfer learning and
hyperparameter optimization. We then propose a new taxonomy that provides a
more comprehensive breakdown of the space of meta-learning methods today. We
survey promising applications and successes of meta-learning such as few-shot
learning and reinforcement learning. Finally, we discuss outstanding challenges
and promising areas for future research
