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One-Shot methods have evolved into one of the most popular methods in NeuralArchitecture Search (NAS) due to weight sharing and single training of asupernet. However, existing methods generally suffer from two issues:predetermined number of channels in each layer which is suboptimal; and modelaveraging effects and poor ranking correlation caused by weight coupling andcontinuously expanding search space. To explicitly address these issues, inthis paper, a Broadening-and-Shrinking One-Shot NAS (BS-NAS) framework isproposed, in which `broadening' refers to broadening the search space with aspring block enabling search for numbers of channels during training of thesupernet; while `shrinking' refers to a novel shrinking strategy graduallyturning off those underperforming operations. The above innovations broaden thesearch space for wider representation and then shrink it by gradually removingunderperforming operations, followed by an evolutionary algorithm toefficiently search for the optimal architecture. Extensive experiments onImageNet illustrate the effectiveness of the proposed BS-NAS as well as thestate-of-the-art performance.

BS-NAS: Broadening-and-Shrinking One-Shot NAS with Searchable Numbers of Channels