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

22 Mar 2020 Zan Shen Jiang Qian Bojin Zhuang Shaojun Wang Jing Xiao

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... (read more)

PDF Abstract
No code implementations yet. Submit your code now

Results from the Paper


  Submit results from this paper to get state-of-the-art GitHub badges and help the community compare results to other papers.

Methods used in the Paper