  This article reviews meta-learning also known as learning-to-learn which
seeks rapid and accurate model adaptation to unseen tasks with applications in
highly automated AI, few-shot learning, natural language processing and
robotics. Unlike deep learning, meta-learning can be applied to few-shot
high-dimensional datasets and considers further improving model generalization
to unseen tasks. Deep learning is focused upon in-sample prediction and
meta-learning concerns model adaptation for out-of-sample prediction.
Meta-learning can continually perform self-improvement to achieve highly
autonomous AI. Meta-learning may serve as an additional generalization block
complementary for original deep learning model. Meta-learning seeks adaptation
of machine learning models to unseen tasks which are vastly different from
trained tasks. Meta-learning with coevolution between agent and environment
provides solutions for complex tasks unsolvable by training from scratch.
Meta-learning methodology covers a wide range of great minds and thoughts. We
briefly introduce meta-learning methodologies in the following categories:
black-box meta-learning, metric-based meta-learning, layered meta-learning and
Bayesian meta-learning framework. Recent applications concentrate upon the
integration of meta-learning with other machine learning framework to provide
feasible integrated problem solutions. We briefly present recent meta-learning
advances and discuss potential future research directions.
