# A Survey of Deep Meta-Learning

arXiv: https://arxiv.org/abs/2010.03522 · HTML: https://mlsanity.com/paper/2010.03522/

## Key facts

- Cited by 100 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 7 Oct 2020.

## Built on by (most cited first, 30 of 100)

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- [High-Accuracy Model-Based Reinforcement Learning, a Survey](https://mlsanity.com/paper/2107.08241/) (2021)
- [Meta-Reinforcement Learning for the Tuning of PI Controllers: An Offline Approach](https://mlsanity.com/paper/2203.09661/) (2022)
- [Domain Generalization in Machine Learning Models for Wireless Communications: Concepts, State-of-the-Art, and Open Issues](https://mlsanity.com/paper/2303.08106/) (2023)
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- [Towards Evaluating Adaptivity of Model-Based Reinforcement Learning Methods](https://mlsanity.com/paper/2204.11464/) (2022)
- [Lessons learned from the NeurIPS 2021 MetaDL challenge: Backbone fine-tuning without episodic meta-learning dominates for few-shot learning image classification](https://mlsanity.com/paper/2206.08138/) (2022)
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- [Multi-Task Meta Learning: learn how to adapt to unseen tasks](https://mlsanity.com/paper/2210.06989/) (2022)
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- [Zero-shot Meta-learning for Tabular Prediction Tasks with Adversarially Pre-trained Transformer](https://mlsanity.com/paper/2502.04573/) (2025)
- [Stateless Neural Meta-Learning using Second-Order Gradients](https://mlsanity.com/paper/2104.10527/) (2021)
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## Abstract (arXiv)

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.