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RARTS: a Relaxed Architecture Search Method

2020-08-10 04:55:51
Fanghui Xue, Yingyong Qi, Jack Xin
arXiv_CV
     
Abstract

Differentiable architecture search (DARTS) is an effective method for data-driven neural network design based on solving a bilevel optimization problem. In this paper, we formulate a single level alternative and a relaxed architecture search (RARTS) method that utilizes training and validation datasets in architecture learning without involving mixed second derivatives of the corresponding loss functions. Through weight/architecture variable splitting and Gauss-Seidel iterations, the core algorithm outperforms DARTS significantly in accuracy and search efficiency, as shown in both a solvable model and CIFAR-10 based architecture search. Our model continues to out-perform DARTS upon transfer to ImageNet and is on par with recent variants of DARTS even though our innovation is purely on the training algorithm.

Abstract (translated)

URL

https://arxiv.org/abs/2008.03901

PDF

https://arxiv.org/pdf/2008.03901.pdf

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