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arXiv · 2007.16112

Neural Architecture Search as Sparse Supernet

Yan Wu·Aoming Liu·Zhiwu Huang·Siwei Zhang·Luc Van Gool
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Abstract

This paper aims at enlarging the problem of Neural Architecture Search (NAS) from Single-Path and Multi-Path Search to automated Mixed-Path Search. In particular, we model the NAS problem as a sparse supernet using a new continuous architecture representation with a mixture of sparsity constraints. The sparse supernet enables us 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 Convolutional Neural Network and Recurrent Neural Network search demonstrate that the proposed method is capable of searching for compact, general and powerful neural architectures.

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Yan Wu, Aoming Liu, Zhiwu Huang, Siwei Zhang, Luc Van Gool. 2020-07-31. Neural Architecture Search as Sparse Supernet. https://arxiv.org/abs/2007.16112

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