Deep Neural Networks (DNNs) have been successfully applied to a wide range of problems. However, two main limitations are commonly pointed out. The first one is that they require long time to design. The other is that they heavily rely on labelled data, which can sometimes be costly and hard to obtain. In order to address the first problem, neuroevolution has been proved to be a plausible option to automate the design of DNNs. As for the second problem, self-supervised learning has been used to leverage unlabelled data to learn representations. Our goal is to study how neuroevolution can help self-supervised learning to bridge the gap to supervised learning in terms of performance. In this work, we propose a framework that is able to evolve deep neural networks using self-supervised learning. Our results on the CIFAR-10 dataset show that it is possible to evolve adequate neural networks while reducing the reliance on labelled data. Moreover, an analysis to the structure of the evolved networks suggests that the amount of labelled data fed to them has less effect on the structure of networks that learned via self-supervised learning, when compared to individuals that relied on supervised learning.

翻译：深度神经网络（DNNs）已成功应用于众多领域。然而，其普遍存在两个主要局限：一是网络设计耗时较长；二是严重依赖标注数据，而标注数据往往成本高昂且难以获取。针对第一个问题，神经进化已被证明是自动化设计DNNs的可行方案。对于第二个问题，自监督学习可利用未标注数据学习表征。本研究旨在探索神经进化如何协助自监督学习在性能层面缩小与监督学习的差距。我们提出了一种基于自监督学习的深度神经网络进化框架。在CIFAR-10数据集上的实验表明，该框架能够在降低对标注数据依赖的同时进化出合适的神经网络。进一步对进化网络结构的分析显示：与依赖监督学习的个体相比，通过自监督学习获得的网络结构受标注数据量的影响更小。