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European Conference on Computer Vision

ECCV 2022: Computer Vision – ECCV 2022 pp 691–708Cite as

Meta-GF: Training Dynamic-Depth Neural Networks Harmoniously

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Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13671))

Abstract

Most state-of-the-art deep neural networks use static inference graphs, which makes it impossible for such networks to dynamically adjust the depth or width of the network according to the complexity of the input data. Different from these static models, depth-adaptive neural networks, e.g. the multi-exit networks, aim at improving the computation efficiency by conducting adaptive inference conditioned on the input. To achieve adaptive inference, multiple output exits are attached at different depths of the multi-exit networks. Unfortunately, these exits usually interfere with each other in the training stage. The interference would reduce performance of the models and cause negative influences on the convergence speed. To address this problem, we investigate the gradient conflict of these multi-exit networks, and propose a novel meta-learning based training paradigm namely Meta-GF (meta gradient fusion) to harmoniously train these exits. Different from existing approaches, Meta-GF takes account of the importances of the shared parameters to each exit, and fuses the gradients of each exit by the meta-learned weights. Experimental results on CIFAR and ImageNet verify the effectiveness of the proposed method. Furthermore, the proposed Meta-GF requires no modification on the network structures and can be directly combined with previous training techniques. The code is available at https://github.com/SYVAE/MetaGF.

Y. Sun and J. Li—Co-first authors with equal contributions.

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Acknowledgement

This work was supported by the National Natural Science Foundation of China (NSFC) under Grants 61825305 and 61973311.

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Authors and Affiliations

  1. The College of Intelligence Science and Technology, National University of Defense Technology, Changsha, 410000, China

    Yi Sun, Jian Li & Xin Xu

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  1. Yi Sun
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  2. Jian Li
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  3. Xin Xu
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Corresponding author

Correspondence to Xin Xu .

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Editors and Affiliations

  1. Tel Aviv University, Tel Aviv, Israel

    Shai Avidan

  2. University College London, London, UK

    Gabriel Brostow

  3. Google AI, Accra, Ghana

    Moustapha Cissé

  4. University of Catania, Catania, Italy

    Giovanni Maria Farinella

  5. Facebook (United States), Menlo Park, CA, USA

    Tal Hassner

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Sun, Y., Li, J., Xu, X. (2022). Meta-GF: Training Dynamic-Depth Neural Networks Harmoniously. In: Avidan, S., Brostow, G., Cissé, M., Farinella, G.M., Hassner, T. (eds) Computer Vision – ECCV 2022. ECCV 2022. Lecture Notes in Computer Science, vol 13671. Springer, Cham. https://doi.org/10.1007/978-3-031-20083-0_41

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  • DOI: https://doi.org/10.1007/978-3-031-20083-0_41

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