DSNAS: Direct Neural Architecture Search without Parameter Retraining

• URL: http://arxiv.org/abs/2002.09128v2
• Date: Wed, 1 Apr 2020 00:31:37 GMT
• Title: DSNAS: Direct Neural Architecture Search without Parameter Retraining
• Authors: Shoukang Hu, Sirui Xie, Hehui Zheng, Chunxiao Liu, Jianping Shi, Xunying Liu, Dahua Lin
• Abstract summary: We propose a new problem definition for NAS, task-specific end-to-end, based on this observation. We propose DSNAS, an efficient differentiable NAS framework that simultaneously optimize architecture and parameters with a low-biased Monte Carlo estimate. DSNAS successfully discovers networks with comparable accuracy (74.4%) on ImageNet in 420 GPU hours, reducing the total time by more than 34%.
• Score: 112.02966105995641
• License: http://creativecommons.org/publicdomain/zero/1.0/
• Abstract: If NAS methods are solutions, what is the problem? Most existing NAS methods require two-stage parameter optimization. However, performance of the same architecture in the two stages correlates poorly. In this work, we propose a new problem definition for NAS, task-specific end-to-end, based on this observation. We argue that given a computer vision task for which a NAS method is expected, this definition can reduce the vaguely-defined NAS evaluation to i) accuracy of this task and ii) the total computation consumed to finally obtain a model with satisfying accuracy. Seeing that most existing methods do not solve this problem directly, we propose DSNAS, an efficient differentiable NAS framework that simultaneously optimizes architecture and parameters with a low-biased Monte Carlo estimate. Child networks derived from DSNAS can be deployed directly without parameter retraining. Comparing with two-stage methods, DSNAS successfully discovers networks with comparable accuracy (74.4%) on ImageNet in 420 GPU hours, reducing the total time by more than 34%. Our implementation is available at https://github.com/SNAS-Series/SNAS-Series.

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