Despite its astounding success in learning deeper multi-dimensional data, the performance of deep learning declines on new unseen tasks mainly due to its focus on same-distribution prediction. Moreover, deep learning is notorious for poor generalization from few samples. Meta-learning is a promising approach that addresses these issues by adapting to new tasks with few-shot datasets. This survey first briefly introduces meta-learning and then investigates state-of-the-art meta-learning methods and recent advances in: (i) metric-based, (ii) memory-based, (iii), and learning-based methods. Finally, current challenges and insights for future researches are discussed.

尽管深度学习在学习更深层次的多维数据方面取得了惊人的成功，但深度学习在新的未见过的任务上的性能下降主要是由于它专注于同分布预测。此外，深度学习因样本较少而泛化能力较差而臭名昭著。元学习是一种很有前途的方法，它通过使用少量数据集适应新任务来解决这些问题。本调查首先简要介绍元学习，然后研究最先进的元学习方法以及以下方面的最新进展：(i) 基于度量、(ii) 基于记忆、(iii) 和基于学习的方法。最后，讨论了当前的挑战和对未来研究的见解。