Abstract
Artificial Neural Networks (ANNs) are computational models inspired by the central nervous system (especially the brain) of animals and are used to estimate or generate unknown approximation functions that rely on a large number of inputs. The Capsule Neural Network [1] is a novel structure of Convolutional Neural Networks (CNN) which simulates the visual processing system of the human brain. In this paper, we introduce a psychological theory which is called Cognitive Consistency to optimize the routing algorithm of Capsnet to make it more close to the working pattern of the human brain. Our experiments show that progress had been made compared with the baseline.
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References
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Acknowledgment
At the point of finishing this paper, we would like to express our sincere thanks to the authors of Dynamic routing between capsules [1] who had made significant breakthroughs and outstanding contributions in exploring the new architecture of neural networks. At the same time, we also have to thank Professor. Razi and Professor. Bakke who gave guidance and support to us during the process of completing this paper.
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Li, H., Wang, Y. (2020). Cognitive Consistency Routing Algorithm of Capsule-Network. In: Arai, K., Kapoor, S. (eds) Advances in Computer Vision. CVC 2019. Advances in Intelligent Systems and Computing, vol 944. Springer, Cham. https://doi.org/10.1007/978-3-030-17798-0_45
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DOI: https://doi.org/10.1007/978-3-030-17798-0_45
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