Understanding and Accelerating Neural Architecture Search with Training-Free and Theory-Grounded Metrics

• URL: http://arxiv.org/abs/2108.11939v1
• Date: Thu, 26 Aug 2021 17:52:07 GMT
• Title: Understanding and Accelerating Neural Architecture Search with Training-Free and Theory-Grounded Metrics
• Authors: Wuyang Chen, Xinyu Gong, Yunchao Wei, Humphrey Shi, Zhicheng Yan, Yi Yang, Zhangyang Wang
• Abstract summary: This work targets designing a principled and unified training-free framework for Neural Architecture Search (NAS) NAS has been explosively studied to automate the discovery of top-performer neural networks, but suffers from heavy resource consumption and often incurs search bias due to truncated training or approximations. We present a unified framework to understand and accelerate NAS, by disentangling "TEG" characteristics of searched networks.
• Score: 117.4281417428145
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: This work targets designing a principled and unified training-free framework for Neural Architecture Search (NAS), with high performance, low cost, and in-depth interpretation. NAS has been explosively studied to automate the discovery of top-performer neural networks, but suffers from heavy resource consumption and often incurs search bias due to truncated training or approximations. Recent NAS works start to explore indicators that can predict a network's performance without training. However, they either leveraged limited properties of deep networks, or the benefits of their training-free indicators are not applied to more extensive search methods. By rigorous correlation analysis, we present a unified framework to understand and accelerate NAS, by disentangling "TEG" characteristics of searched networks - Trainability, Expressivity, Generalization - all assessed in a training-free manner. The TEG indicators could be scaled up and integrated with various NAS search methods, including both supernet and single-path approaches. Extensive studies validate the effective and efficient guidance from our TEG-NAS framework, leading to both improved search accuracy and over 2.3x reduction in search time cost. Moreover, we visualize search trajectories on three landscapes of "TEG" characteristics, observing that while a good local minimum is easier to find on NAS-Bench-201 given its simple topology, balancing "TEG" characteristics is much harder on the DARTS search space due to its complex landscape geometry. Our code is available at https://github.com/VITA-Group/TEGNAS.

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