Neural Architecture Search as Sparse Supernet

• URL: http://arxiv.org/abs/2007.16112v2
• Date: Wed, 31 Mar 2021 16:35:16 GMT
• Title: Neural Architecture Search as Sparse Supernet
• Authors: Yan Wu, Aoming Liu, Zhiwu Huang, Siwei Zhang, Luc Van Gool
• Abstract summary: This paper aims at enlarging the problem of Neural Architecture Search (NAS) from Single-Path and Multi-Path Search to automated Mixed-Path Search. 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.
• Score: 78.09905626281046
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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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