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Neural Architecture Search as Sparse Supernet

2020-07-31 14:51:52
Yan Wu, Aoming Liu, Zhiwu Huang, Siwei Zhang, Luc Van Gool
arXiv_CV
         
Abstract

This paper aims at enlarging the problem of Neural Architecture Search from Single-Path and Multi-Path Search to automated Mixed-Path Search. In particular, we model the new problem as a sparse supernet with a new continuous architecture representation using a mixture of sparsity constraints, i.e., Sparse Group Lasso. The sparse supernet is expected to automatically achieve sparsely-mixed paths upon a compact set of nodes. To optimize the proposed sparse supernet, we exploit a hierarchical accelerated proximal gradient algorithm within a bi-level optimization framework. Extensive experiments on CIFAR-10, CIFAR-100, Tiny ImageNet and ImageNet demonstrate that the proposed methodology is capable of searching for compact, general and powerful neural architectures.

Abstract (translated)

URL

https://arxiv.org/abs/2007.16112

PDF

https://arxiv.org/pdf/2007.16112.pdf

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