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.

本论文旨在提供关于 Deep Meta-Learning 的理论基础以及总结关键的度量、模型和优化技术，同时也指出了性能评估和计算成本等主要挑战。

深度元学习综述

A Survey of Deep Meta-Learning

Few-shot learning remains challenging for meta-learning that learns a
learning algorithm (meta-learner) from many related tasks. In this work, we
argue that this is due to the lack of a good representation for meta-learning,
and propose deep meta-learning to integrate the representation power of deep
learning into meta-learning. The framework is composed of three modules, a
concept generator, a meta-learner, and a concept discriminator, which are
learned jointly. The concept generator, e.g. a deep residual net, extracts a
representation for each instance that captures its high-level concept, on which
the meta-learner performs few-shot learning, and the concept discriminator
recognizes the concepts. By learning to learn in the concept space rather than
in the complicated instance space, deep meta-learning can substantially improve
vanilla meta-learning, which is demonstrated on various few-shot image
recognition problems. For example, on 5-way-1-shot image recognition on
CIFAR-100 and CUB-200, it improves Matching Nets from 50.53% and 56.53% to
58.18% and 63.47%, improves MAML from 49.28% and 50.45% to 56.65% and 64.63%,
and improves Meta-SGD from 53.83% and 53.34% to 61.62% and 66.95%,
respectively.

通过引入深层元学习作为组成部分，将深度学习的表示能力融入到元学习中，通过建立一个刻画高级概念的表征空间，可以有效地改进原先的元学习算法，从而实现对于各种少样本图像识别问题中识别率的提升。