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 quite limited. Meta-learning is one approach to
address this issue, by enabling the network to learn how to learn. The exciting
field of Deep Meta-Learning advances at great speed, but lacks a unified,
insightful overview of current techniques. This work presents just that. 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.Comment: Extended version of book chapter in 'Metalearning: Applications to
Automated Machine Learning and Data Mining' (2nd edition, forthcoming
