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arXiv · 2003.12238

MiLeNAS: Efficient Neural Architecture Search via Mixed-Level Reformulation

Chaoyang He·Haishan Ye·Li Shen·Tong Zhang
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Abstract

Many recently proposed methods for Neural Architecture Search (NAS) can be formulated as bilevel optimization. For efficient implementation, its solution requires approximations of second-order methods. In this paper, we demonstrate that gradient errors caused by such approximations lead to suboptimality, in the sense that the optimization procedure fails to converge to a (locally) optimal solution. To remedy this, this paper proposes \mldas, a mixed-level reformulation for NAS that can be optimized efficiently and reliably. It is shown that even when using a simple first-order method on the mixed-level formulation, \mldas\ can achieve a lower validation error for NAS problems. Consequently, architectures obtained by our method achieve consistently higher accuracies than those obtained from bilevel optimization. Moreover, \mldas\ proposes a framework beyond DARTS. It is upgraded via model size-based search and early stopping strategies to complete the search process in around 5 hours. Extensive experiments within the convolutional architecture search space validate the effectiveness of our approach.

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Chaoyang He, Haishan Ye, Li Shen, Tong Zhang. 2020-03-27. MiLeNAS: Efficient Neural Architecture Search via Mixed-Level Reformulation. https://arxiv.org/abs/2003.12238

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