BS-NAS：具有可搜索通道数的扩展和收缩单次NAS
BS-NAS: Broadening-and-Shrinking One-Shot NAS with Searchable Numbers of Channels
摘要：
One-Shot方法由于超网的权重共享和单次训练而发展成为神经结构搜索（NAS）中最流行的方法之一。然而，现有的方法通常遭受两个问题：预定数量的通道，在每一层，这是次优的;和模型平均效应和差的排名相关性所造成的权重耦合和不断扩大的搜索空间。为了明确地解决这些问题，在本文中，提出了一个扩大和缩小的单次NAS（BS-NAS）的框架，其中“扩大”是指扩大搜索空间与弹簧块，使搜索通道的数量在训练过程中的超网，而“缩小”是指一种新的收缩策略逐渐关闭那些表现不佳的操作。上述创新扩大了搜索空间以获得更广泛的表示，然后通过逐渐去除表现不佳的操作来缩小搜索空间，然后使用进化算法来有效地搜索最佳架构。在ImageNet上的大量实验表明了所提出的BS-NAS的有效性以及最先进的性能。
英文摘要：
One-Shot methods have evolved into one of the most popular methods in Neural Architecture Search (NAS) due to weight sharing and single training of a supernet. However, existing methods generally suffer from two issues: predetermined number of channels in each layer which is suboptimal; and model averaging effects and poor ranking correlation caused by weight coupling and continuously expanding search space. To explicitly address these issues, in this paper, a Broadening-and-Shrinking One-Shot NAS (BS-NAS) framework is proposed, in which `broadening' refers to broadening the search space with a spring block enabling search for numbers of channels during training of the supernet; while `shrinking' refers to a novel shrinking strategy gradually turning off those underperforming operations. The above innovations broaden the search space for wider representation and then shrink it by gradually removing underperforming operations, followed by an evolutionary algorithm to efficiently search for the optimal architecture. Extensive experiments on ImageNet illustrate the effectiveness of the proposed BS-NAS as well as the state-of-the-art performance.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
