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Volume 19 Issue 2
April  2022
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Zhiqiang Chen, Ting-Bing Xu, Jinpeng Li, Huiguang He. Sharing Weights in Shallow Layers via Rotation Group Equivariant Convolutions. Machine Intelligence Research, vol. 19, no. 2, pp.115-126, 2022. https://doi.org/10.1007/s11633-022-1324-5
Citation: Zhiqiang Chen, Ting-Bing Xu, Jinpeng Li, Huiguang He. Sharing Weights in Shallow Layers via Rotation Group Equivariant Convolutions. Machine Intelligence Research, vol. 19, no. 2, pp.115-126, 2022. https://doi.org/10.1007/s11633-022-1324-5
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Sharing Weights in Shallow Layers via Rotation Group Equivariant Convolutions

doi: 10.1007/s11633-022-1324-5
  • 1.

    Research Center for Brain-inspired Intelligence, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China

  • 2.

    School of Instrumentation and Optoelectronic Engineering, Beihang University, Beijing 100191, China

  • 3.

    Ningbo HwaMei Hospital, University of Chinese Academy of Sciences, Ningbo 315012, China

  • 4.

    Center for Excellence in Brain Science and Intelligence Technology, Chinese Academy of Sciences, Beijing 100190, China

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  • Author Bio:

    Zhiqiang Chen received the B. Sc. degree in intelligence science and technology from College of Automation, University of Science and Technology Beijing, China in 2014. He received the M. Sc. and Ph. D. degrees in pattern recognition and intelligence systems with Institute of Automation, Chinese Academy of Sciences, China in 2017 and 2021, respectively. He is currently a postdoctoral fellow with Beijing Academy of Artificial Intelligence and the Institute of Automation, Chinese Academy of Sciences, China.His research interests include machine learning, brain-inspired intelligence, medical image, and computer vision.E-mail: chenzhiqiang2014@ia.ac.cnORCID iD: 0000-0003-0242-6577

    Ting-Bing Xu received the B. Sc. degree in automation from China University of Petroleum, China in 2014, and the Ph. D. degree in computer applied technology from National Laboratory of Pattern Recognition (NLPR), Institute of Automation, Chinese Academy of Sciences, China in 2020. He was a visiting researcher with Department of Computer Science and Intelligent Systems, Osaka Prefecture University, Japan in 2018. He is currently a postdoctoral fellow with School of Instrumentation and Optoelectronic Engineering, Beihang University, China. His research interests include deep learning, machine learning, pattern recognition, handwriting recognition, and computer vision.E-mail: tingbing_xu@buaa.edu.cn

    Jinpeng Li received the B. Eng. and M. Eng. degrees in automatic control from University of Science and Technology, China in 2012 and 2015. He received the Ph. D. degree in pattern recognition and intelligent systems from Institute of Automation, Chinese Academy of Sciences, China in 2019. He is now a researcher at Ningbo HwaMei Hospital, University of Chinese Academy of Sciences, China, and Institute of Life and Health Science, University of Chinese Academy of Sciences, China. He has authored or coauthored more than ten peer-reviewed papers in international journals and conferences. His research interests include pattern recognition, machine learning, deep learning, transfer learning algorithms and their applications in epidemiology and medical image analysis. E-mail: lijinpeng@ucas.ac.cn

    Huiguang He received the B. Sc. and M. Sc. degrees from Dalian Maritime University (DMU), China in 1994 and 1997, respectively, and the Ph. D. degree (Hons.) in pattern recognition and intelligent systems from Institute of Automation, Chinese Academy of Sciences, China. From 1997 to 1999, he was an associate lecturer with DMU. From 2003 to 2004, he was a post-doctoral researcher with University of Rochester, USA. From 2014 to 2015, he was a visiting professor with University of North Carolina at Chapel Hill, USA. He is currently a full professor with Institute of Automation, Chinese Academy of Sciences. His research has been supported by several research grants from National Science Foundation of China. He has authored or co-authored more than 180 peer-reviewed papers. Dr. He is an Excellent Member of Youth Innovation Promotion Association, Chinese Academy of Sciences in 2016. He was a recipient of the Excellent Ph. D. dissertation of Chinese Academy of Sciences in 2004, the National Science and Technology Award in 2003 and 2004, the Beijing Science and Technology Award in 2002 and 2003, the K.C. Wong Education Prizes in 2007 and 2009, and the Jia-Xi Lu Young Talent Prize in 2009. His research interests include pattern recognition, medical image processing, and brain-computer interfaces (BCI).E-mail: huiguang.he@ia.ac.cn (Corresponding author)ORCID iD: 0000-0002-0684-1711

  • Received Date: 2021-12-01
  • Accepted Date: 2022-02-07
  • Publish Online: 2022-03-07
  • Publish Date: 2022-04-01
    Abstract
  • The convolution operation possesses the characteristic of translation group equivariance. To achieve more group equivariances, rotation group equivariant convolutions (RGEC) are proposed to acquire both translation and rotation group equivariances. However, previous work paid more attention to the number of parameters and usually ignored other resource costs. In this paper, we construct our networks without introducing extra resource costs. Specifically, a convolution kernel is rotated to different orientations for feature extractions of multiple channels. Meanwhile, much fewer kernels than previous works are used to ensure that the output channel does not increase. To further enhance the orthogonality of kernels in different orientations, we construct the non-maximum-suppression loss on the rotation dimension to suppress the other directions except the most activated one. Considering that the low-level-features benefit more from the rotational symmetry, we only share weights in the shallow layers (SWSL) via RGEC. Extensive experiments on multiple datasets (i.e., ImageNet, CIFAR, and MNIST) demonstrate that SWSL can effectively benefit from the higher-degree weight sharing and improve the performances of various networks, including plain and ResNet architectures. Meanwhile, the convolutional kernels and parameters are much fewer (e.g., 75%, 87.5% fewer) in the shallow layers, and no extra computation costs are introduced.

     

    • 1 http://yann.lecun.com/exdb/mnist/
    3 http://www.image-net.org/challenges/LSVRC/
    2 http://www.cs.toronto.edu/kriz/cifar.html3 http://www.image-net.org/challenges/LSVRC/
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