Universally slimmable networks and improved training techniques

Jiahui Yu, Thomas Huang

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Slimmable networks are a family of neural networks that can instantly adjust the runtime width. The width can be chosen from a predefined widths set to adaptively optimize accuracy-efficiency trade-offs at runtime. In this work, we propose a systematic approach to train universally slimmable networks (US-Nets), extending slimmable networks to execute at arbitrary width, and generalizing to networks both with and without batch normalization layers. We further propose two improved training techniques for US-Nets, named the sandwich rule and inplace distillation, to enhance training process and boost testing accuracy. We show improved performance of universally slimmable MobileNet v1 and MobileNet v2 on ImageNet classification task, compared with individually trained ones and 4-switch slimmable network baselines. We also evaluate the proposed US-Nets and improved training techniques on tasks of image super-resolution and deep reinforcement learning. Extensive ablation experiments on these representative tasks demonstrate the effectiveness of our proposed methods. Our discovery opens up the possibility to directly evaluate FLOPs-Accuracy spectrum of network architectures. Code and models are available at: Url{https://github.com/JiahuiYu/slimmable-networks}.

Original languageEnglish (US)
Title of host publicationProceedings - 2019 International Conference on Computer Vision, ICCV 2019
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages1803-1811
Number of pages9
ISBN (Electronic)9781728148038
DOIs
StatePublished - Oct 2019
Event17th IEEE/CVF International Conference on Computer Vision, ICCV 2019 - Seoul, Korea, Republic of
Duration: Oct 27 2019Nov 2 2019

Publication series

NameProceedings of the IEEE International Conference on Computer Vision
Volume2019-October
ISSN (Print)1550-5499

Conference

Conference17th IEEE/CVF International Conference on Computer Vision, ICCV 2019
CountryKorea, Republic of
CitySeoul
Period10/27/1911/2/19

ASJC Scopus subject areas

  • Software
  • Computer Vision and Pattern Recognition

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  • Cite this

    Yu, J., & Huang, T. (2019). Universally slimmable networks and improved training techniques. In Proceedings - 2019 International Conference on Computer Vision, ICCV 2019 (pp. 1803-1811). [9009445] (Proceedings of the IEEE International Conference on Computer Vision; Vol. 2019-October). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ICCV.2019.00189