With the development of deep learning, the design of an appropriate network structure becomes fundamental. In recent years, the successful practice of Neural Architecture Search (NAS) has indicated that an automated design of the network structure can efficiently replace the design performed by human experts. Most NAS algorithms make the assumption that the overall structure of the network is linear and focus solely on accuracy to assess the performance of candidate networks. This paper introduces a novel NAS algorithm based on a multi-objective modeling of the network design problem to design accurate Convolutional Neural Networks (CNNs) with a small structure. The proposed algorithm makes use of a graph-based representation of the solutions which enables a high flexibility in the automatic design. Furthermore, the proposed algorithm includes novel ad-hoc crossover and mutation operators. We also propose a mechanism to accelerate the evaluation of the candidate solutions. Experimental results demonstrate that the proposed NAS approach can design accurate neural networks with limited size.

中文翻译：

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基于图中图的多目标进化方法用于卷积神经网络的神经架构搜索

随着深度学习的发展，设计合适的网络结构变得至关重要。近年来，神经架构搜索（NAS）的成功实践表明，网络结构的自动化设计可以有效地替代人类专家的设计。大多数 NAS 算法都假设网络的整体结构是线性的，并且只关注准确性来评估候选网络的性能。本文介绍了一种基于网络设计问题的多目标建模的新型 NAS 算法，以设计具有小结构的精确卷积神经网络 (CNN)。所提出的算法利用了解决方案的基于图形的表示，从而在自动设计中实现了高度的灵活性。此外，所提出的算法包括新颖的临时交叉和变异算子。我们还提出了一种加速候选解决方案评估的机制。实验结果表明，所提出的 NAS 方法可以设计出尺寸有限的精确神经网络。