Neural Architecture Search as Sparse Supernet
Yan Wu, Aoming Liu, Zhiwu Huang, Siwei Zhang, Luc Van Gool
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.
BibTeX
@inproceedings{aaai2021_neuralarchitectu,
title = {Neural Architecture Search as Sparse Supernet},
author = {Yan Wu and Aoming Liu and Zhiwu Huang and Siwei Zhang and Luc Van Gool},
booktitle = {AAAI 2021},
year = {2021}
}