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arXiv · 2108.05866

Improving Ranking Correlation of Supernet with Candidates Enhancement and Progressive Training

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.

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Ziwei Yang, Ruyi Zhang, Zhi Yang, Xubo Yang, Lei Wang, Zheyang Li. 2021-08-12. Improving Ranking Correlation of Supernet with Candidates Enhancement and Progressive Training. https://arxiv.org/abs/2108.05866

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