Neural architecture search (NAS) has attracted increasing attention. In recent years, individual search methods have been replaced by weight-sharing search methods for higher search efficiency, but the latter methods often suffer lower instability. This article provides a literature review on these methods and owes this issue to the optimization gap . From this perspective, we summarize existing approaches into several categories according to their efforts in bridging the gap, and we analyze both advantages and disadvantages of these methodologies. Finally, we share our opinions on the future directions of NAS and AutoML. Due to the expertise of the authors, this article mainly focuses on the application of NAS to computer vision problems.

中文翻译：

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权重共享神经架构搜索：缩小优化差距的战斗

神经架构搜索（NAS）引起了越来越多的关注。最近几年，个人搜索方法已被替换为重量分担搜索效率更高的搜索方法，但后一种方法通常具有较低的不稳定性。本文对这些方法进行了文献综述，并将这个问题归咎于优化差距. 从这个角度来看，我们根据它们在弥合差距方面的努力将现有方法总结为几类，并分析这些方法的优缺点。最后，我们分享我们对 NAS 和 AutoML 未来发展方向的看法。由于作者的专业性，本文主要关注 NAS 在计算机视觉问题上的应用。