  Deep neural networks can achieve great successes when presented with large
data sets and sufficient computational resources. However, their ability to
learn new concepts quickly is limited. Meta-learning is one approach to address
this issue, by enabling the network to learn how to learn. The field of Deep
Meta-Learning advances at great speed, but lacks a unified, in-depth overview
of current techniques. With this work, we aim to bridge this gap. After
providing the reader with a theoretical foundation, we investigate and
summarize key methods, which are categorized into i)~metric-, ii)~model-, and
iii)~optimization-based techniques. In addition, we identify the main open
challenges, such as performance evaluations on heterogeneous benchmarks, and
reduction of the computational costs of meta-learning.
