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arXiv · 2001.01431

Deeper Insights into Weight Sharing in Neural Architecture Search

Yuge Zhang·Zejun Lin·Junyang Jiang·Quanlu Zhang·Yujing Wang·Hui Xue·Chen Zhang·Yaming Yang
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Abstract

With the success of deep neural networks, Neural Architecture Search (NAS) as a way of automatic model design has attracted wide attention. As training every child model from scratch is very time-consuming, recent works leverage weight-sharing to speed up the model evaluation procedure. These approaches greatly reduce computation by maintaining a single copy of weights on the super-net and share the weights among every child model. However, weight-sharing has no theoretical guarantee and its impact has not been well studied before. In this paper, we conduct comprehensive experiments to reveal the impact of weight-sharing: (1) The best-performing models from different runs or even from consecutive epochs within the same run have significant variance; (2) Even with high variance, we can extract valuable information from training the super-net with shared weights; (3) The interference between child models is a main factor that induces high variance; (4) Properly reducing the degree of weight sharing could effectively reduce variance and improve performance.

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Yuge Zhang, Zejun Lin, Junyang Jiang, Quanlu Zhang, Yujing Wang, Hui Xue, Chen Zhang, Yaming Yang. 2020-01-06. Deeper Insights into Weight Sharing in Neural Architecture Search. https://arxiv.org/abs/2001.01431

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