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Evaluating shallow capsule networks on complex images

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Abstract

Useful real-life images meant for computer vision applications appear in complex forms with varied backgrounds. Convolutional neural networks with their invariance outperforms capsule networks (CapsNets) on such images. Recent research to improve the accuracy of CapsNets on complex images such as CIFAR10 involves the increase in depth of the networks. However, smaller architectures with efficient feature extractors can perform comparatively well with additional advantages of having less computational cost, reduced risk of overfitting, increased suitability for deployment on devices with smaller memories, and enhanced feasibility to perform distributed training across servers and in the cloud. This paper, therefore, proposes a shallow CapsNet based on dynamic routing with a custom squashing function and normalizer to gain all of these advantages. The model is evaluated on three publicly available datasets; fashion-MNIST, CIFAR-10, and tomato datasets. Experimental results show that the proposed model outperforms the original CapsNet by generating fewer parameters and performing comparably well in terms of accuracy with some state-of-the-art multi-lane and ensemble capsule network models.

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Availability of data and material

Freely available online from the cited sources.

Code availability

https://github.com/XifengGuo/CapsNet-Keras.

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Authors and Affiliations

  1. Department of Computer Science and Informatics, University of Energy and Natural Resources, P.O. Box 214, Sunyani, Ghana

    Patrick Kwabena Mensah & Benjamin Asubam Weyori

  2. School of Information and Software Engineering, University of Electronic Science and Technology of China, Chengdu, China

    Mighty Abra Ayidzoe

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  1. Patrick Kwabena Mensah
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  2. Benjamin Asubam Weyori
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  3. Mighty Abra Ayidzoe
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Correspondence to Patrick Kwabena Mensah.

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Mensah, P.K., Weyori, B.A. & Ayidzoe, M.A. Evaluating shallow capsule networks on complex images. Int. j. inf. tecnol. 13, 1047–1057 (2021). https://doi.org/10.1007/s41870-021-00694-y

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