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.
翻译:深神经网络在提供大型数据集和充足的计算资源时可以取得巨大成功。 但是,它们迅速学习新概念的能力是有限的。 元学习是解决这一问题的一种方法,它使网络能够学习如何学习。 深元学习领域是高速进步,但缺乏对当前技术的统一、深入的概览。 通过这项工作,我们的目标是弥合这一差距。 在向读者提供理论基础之后,我们调查并总结了主要方法,这些方法被归类为(i) ~ 计量, (ii) ~ 模型和 (iii) ~ 优化基础技术。 此外,我们确定了主要的公开挑战,如对多种基准的绩效评估和降低元学习的计算成本。