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Multiple Regularization and Analysis of Deep Capsule Network

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Abstract

With the increase of layers in deep capsule networks, the overfitting problem also becomes more serious. Capsule-based regularization methods are important to solve this problem. However, little attention has been paid to this field. To fill this gap, we propose five regularization methods from the following aspects. In capsules represented by vectors, two methods are proposed to modify the existence and properties of their activation vectors by disturbing the length and orientation of the vectors. In capsules represented by tensors, capsule-based layer normalization is proposed to improve dynamic routing. In the training strategy, a warm restart learning rate with probability is used to improve the efficiency of training. In reconstruction, a novel image decoder provides a better regularization effect by using multiscale information of images. These regularization methods are investigated on CIFAR10, CIFAR100, and SVHN. Experiments show that using these regularization methods can effectively improve the generalization performance.

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Funding

The work was supported by the National Natural Science Foundation of China under Grant 61472278, and Major project of Tianjin under Grant 18ZXZNGX00150, and the Key Project of Natural Science Foundation of Tianjin University under Grant 2017ZD13, and the Research Project of Tianjin Municipal Education Commission under Grant 2017KJ255.

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Authors and Affiliations

  1. School of Computer Science and Engineering, Tianjin University of Technology, Tianjin, 300384, China

    Kun Sun, Haixia Xu, Liming Yuan & Xianbin Wen

  2. Key Laboratory of Computer Vision and System, Ministry of Education, Tianjin, 300384, China

    Kun Sun, Haixia Xu, Liming Yuan & Xianbin Wen

  3. Tianjin Key Laboratory of Intelligence Computing and Novel Software Technology, Tianjin, 300384, China

    Kun Sun, Haixia Xu, Liming Yuan & Xianbin Wen

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  1. Kun Sun
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  2. Haixia Xu
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Correspondence to Xianbin Wen.

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Sun, K., Xu, H., Yuan, L. et al. Multiple Regularization and Analysis of Deep Capsule Network. Pattern Anal Applic 25, 711–729 (2022). https://doi.org/10.1007/s10044-022-01070-7

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