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Computer Science > Computer Vision and Pattern Recognition

arXiv:2108.05866 (cs)
[Submitted on 12 Aug 2021]

Title:Improving Ranking Correlation of Supernet with Candidates Enhancement and Progressive Training

Authors:Ziwei Yang, Ruyi Zhang, Zhi Yang, Xubo Yang, Lei Wang, Zheyang Li
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Abstract:One-shot neural architecture search (NAS) applies weight-sharing supernet to reduce the unaffordable computation overhead of automated architecture designing. However, the weight-sharing technique worsens the ranking consistency of performance due to the interferences between different candidate networks. To address this issue, we propose a candidates enhancement method and progressive training pipeline to improve the ranking correlation of supernet. Specifically, we carefully redesign the sub-networks in the supernet and map the original supernet to a new one of high capacity. In addition, we gradually add narrow branches of supernet to reduce the degree of weight sharing which effectively alleviates the mutual interference between sub-networks. Finally, our method ranks the 1st place in the Supernet Track of CVPR2021 1st Lightweight NAS Challenge.
Comments: 5 pages, 2 figures. CVPR2021 NAS challenge
Subjects: Computer Vision and Pattern Recognition (cs.CV)
Cite as: arXiv:2108.05866 [cs.CV]
  (or arXiv:2108.05866v1 [cs.CV] for this version)
  https://doi.org/10.48550/arXiv.2108.05866

Submission history

From: Ziwei Yang [view email]
[v1] Thu, 12 Aug 2021 17:27:10 UTC (255 KB)
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