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SPIN: An Empirical Evaluation on Sharing Parameters of Isotropic Networks

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Computer Vision – ECCV 2022 (ECCV 2022)

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Recent isotropic networks, such as ConvMixer and Vision Transformers, have found significant success across visual recognition tasks, matching or outperforming non-isotropic Convolutional Neural Networks. Isotropic architectures are particularly well-suited to cross-layer weight sharing, an effective neural network compression technique. In this paper, we perform an empirical evaluation on methods for sharing parameters in isotropic networks (SPIN). We present a framework to formalize major weight sharing design decisions and perform a comprehensive empirical evaluation of this design space. Guided by our experimental results, we propose a weight sharing strategy to generate a family of models with better overall efficiency, in terms of FLOPs and parameters versus accuracy, compared to traditional scaling methods alone, for example compressing ConvMixer by \(1.9\times \) while improving accuracy on ImageNet. Finally, we perform a qualitative study to further understand the behavior of weight sharing in isotropic architectures. The code is available at

C-Y. Lin and A. Prabhu—Equal contribution.

C.-Y. Lin—Work done while interning at Apple.

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  1. 1.

    We interchangeably use the terms parameter and weight sharing throughout this paper.


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Lin, CY. et al. (2022). SPIN: An Empirical Evaluation on Sharing Parameters of Isotropic Networks. 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.

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