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.

当使用大量数据集和足够的计算资源时，深度神经网络可以取得巨大的成功。但是，他们快速学习新概念的能力受到限制。通过使网络学习如何学习，元学习是解决此问题的一种方法。领域深层元学习进步很快，但缺乏对当前技术的统一，深入的概述。通过这项工作，我们旨在弥合这一差距。在为读者提供了理论基础之后，我们将研究和总结关键方法，这些方法分为（i）度量，（ii）模型和（iii）基于优化的技术。此外，我们确定了主要的开放挑战，例如对异构基准的性能评估，以及降低元学习的计算成本。