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Research on Capsule Network Optimization Structure by Variable Route Planning

机译：可变路线规划胶囊网络优化结构研究

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摘要

Convolutional Neural Networks (CNN) perform quite good on image classification and object recognition tasks. To solve the problem that CNN is not robust to affine transformation and can not consider the spatial relationship between objects in images, Capsule Network (CapsNet) is proposed and the best accuracy of MNIST dataset has achieved. It shows developability on other datasets and its performance is significantly higher than CNN. However, the data categories in the existing routing algorithms of the CapsNet need to be given in advance, which is very difficult to estimate. In a complex dataset or a small sample dataset, calculating the maximum likelihood solution is more complicated, and it will lead to an inevitable singularity problem. For these shortcomings of the routing algorithm, this paper first proposes a variational routing algorithm. The algorithm does not bring additional computational burden, can automatically determine the most suitable data category, and can effectively avoid over-fitting problems. The algorithm uses the variational distribution to integrate the prior distribution information to increase the regularization limit, maximize the lower bound of the model evidence, and avoid the singularity problem caused by computing the maximum likelihood function. In this paper, the MNIST dataset is classified by a simple CapsNet architecture, and the result is encouraging.
机译：卷积神经网络（CNN）对图像分类和对象识别任务进行了非常好。为了解决CNN不强大地归属变换的问题，并且不能考虑图像中对象之间的空间关系，提出了胶囊网络（CAPSnet），并且已经实现了MNIST数据集的最佳精度。它显示了其他数据集的开发性，其性能明显高于CNN。然而，需要提前给出CapsNet的现有路由算法中的数据类别，这非常难以估计。在复杂的数据集或小样本数据集中，计算最大似然解决方案更加复杂，并且会导致不可避免的奇点问题。对于路由算法的这些缺点，本文首先提出了变分路由算法。该算法不会带来额外的计算负担，可以自动确定最合适的数据类别，可以有效地避免过度拟合的问题。该算法使用变化分布来集成前提分配信息以增加正则化限制，最大化模型证据的下限，并避免通过计算最大似然函数引起的奇点问题。在本文中，MNIST DataSet由简单的CapsNet架构进行分类，结果是令人鼓舞的。

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