Deeper Insights into Weight Sharing in Neural Architecture Search

By Yuge Zhang, Zejun Lin, Junyan Jiang, Quanlu Zhang, Yujing Wang, Hui Xue, Chen Zhang, Yaming Yang. 2020

55 citations recorded by the source corpus.

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.

Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition, Neural Architecture Search & AutoML

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Citations in the corpus

Initialization and Regularization of Factorized Neural Layers (2021, background): “guessing “lottery tickets” (Frankle & Carbin, 2019)—with a prevailing trend of viewing low-rank methods as uncompetitive for compression (Blalock et al., 2020; Zhang et al., 2020; Idelbayev & Carreira-Perpiñán, 2020; Su et al., 2020).”
When NAS Meets Robustness: In Search of Robust Architectures Against Adversarial Attacks (2019, unknown): “Rather than direct evaluating the network on validation dataset as vanilla NAS methods, we find that finetuning the sampled network with adversarial training for only a few epochs can significantly improve the performance, which is also observed in [42].”