BDARS_CapsNet: Bi-Directional Attention Routing Sausage Capsule Network

• Published in: IEEE access 2020, Vol.8, p.59059-59068
• Main Authors: Ning, Xin, Tian, Weijuan, Li, Weijun, Lu, Yueyue, Nie, Shuai, Sun, Linjun, Chen, Ziheng
• Format: Article
• Language: English
• Subjects: Accuracy; Artificial neural networks; attention routing; CapsNet; CNN; Computer vision; covering learning; Disentangled representation; Learning; Representations
• ISSN: 2169-3536; 2169-3536
• DOI: 10.1109/ACCESS.2020.2982782

In order to improve the accuracy of capsule network in disentangled representation, and further expand its application in computer vision, a novel BDARS_CapsNet (bi-directional attention routing sausage capsule network) architecture is proposed in this paper. Firstly, the bi-directional routing, namely bottom-up and top-down attention is used to achieve information feed-forward and feedback mechanism, which contributes to describing the attributes of object entity more accurately and completely. Secondly, inspired by the concept of covering learning, the sausage measure model is introduced into the network. The sausage model measures both the similarities and differences of the capsules and projects them into a more complex curved surface, which makes it possible to approximate any nonlinear function with arbitrary precision and preserving the local responsiveness of capsule entity to the maximum. Finally, the BDARS_CapsNet combines the CNN (Convolutional Neural Network), bi-directional attention routing, and sausage measure into capsule network modeling, and makes full use of high-level category information and low-level vision information; as a result, the reconstruction and classification accuracy is accordingly improved. Experiments demonstrate the effectiveness of proposed information routing, sausage measure, and new framework. Furthermore, the proposed BDARS_CapsNet provides a foundation for future research on disentangled representation learning.