将进化学习与自监督学习结合，自动设计高效神经网络。
Evolutionary Machine Learning meets Self-Supervised Learning: a comprehensive survey
- 用进化算法自动优化自监督学习的网络结构和训练策略。
- 减少对标注数据依赖，提升模型在少样本场景下的性能。
- 适合关注自动化机器学习与低资源学习的研究者。
近年来，将进化机器学习与自监督学习结合的研究数量持续增长。进化机器学习有助于自动化机器学习算法的设计，并生成更可靠的解决方案；而自监督学习在标注数据有限时能有效学习有用特征。这表明两者的结合可同时优化进化过程、自动化深度神经网络设计，并降低对标注数据的需求。然而，目前尚无详细综述系统阐述二者如何协同工作。为此，本文梳理了相关研究，提出一个新研究方向——进化自监督学习，并建立其分类体系。最后，指出该领域的主要挑战，并为未来研究提供方向，以推动该领域的成长与成熟。
原文摘要 · Abstract (English)
The number of studies that combine Evolutionary Machine Learning and self-supervised learning has been growing steadily in recent years. Evolutionary Machine Learning has been shown to help automate the design of machine learning algorithms and to lead to more reliable solutions. Self-supervised learning, on the other hand, has produced good results in learning useful features when labelled data is limited. This suggests that the combination of these two areas can help both in shaping evolutionary processes and in automating the design of deep neural networks, while also reducing the need for labelled data. Still, there are no detailed reviews that explain how Evolutionary Machine Learning and self-supervised learning can be used together. To help with this, we provide an overview of studies that bring these areas together. Based on this growing interest and the range of existing works, we suggest a new sub-area of research, which we call Evolutionary Self-Supervised Learning and introduce a taxonomy for it. Finally, we point out some of the main challenges and suggest directions for future research to help Evolutionary Self-Supervised Learning grow and mature as a field.
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