论文标题
四囊胶囊网络
Quaternion Capsule Networks
论文作者
论文摘要
胶囊是神经元的分组，这些神经元可以代表视觉实体（例如姿势和特征）的复杂信息。在此属性方面，胶囊网络在诸如对象识别诸如看不见的观点之类的挑战性任务中的表现优于CNN，这是通过在姿势信息的高维表示的帮助下学习对象及其部分之间的转换来实现的。在本文中，我们介绍了四囊胶囊（QCN），其中胶囊及其转换的姿势由四元组表示。四季度对阳性锁免疫，具有胶囊旋转表示形式的直接正则化，并且比矩阵所需的参数数少。实验结果表明，QCN可以更好地推广到具有较少参数的新观点，并且在众所周知的基准数据集上使用最新的胶囊体系结构实现了PAR或更好的性能。
Capsules are grouping of neurons that allow to represent sophisticated information of a visual entity such as pose and features. In the view of this property, Capsule Networks outperform CNNs in challenging tasks like object recognition in unseen viewpoints, and this is achieved by learning the transformations between the object and its parts with the help of high dimensional representation of pose information. In this paper, we present Quaternion Capsules (QCN) where pose information of capsules and their transformations are represented by quaternions. Quaternions are immune to the gimbal lock, have straightforward regularization of the rotation representation for capsules, and require less number of parameters than matrices. The experimental results show that QCNs generalize better to novel viewpoints with fewer parameters, and also achieve on-par or better performances with the state-of-the-art Capsule architectures on well-known benchmarking datasets.
