卷积优化的变分自编码聚类方法

传统的变分自编码器将样本展平后直接作为输入数据，当样本为图像数据时，采用这样的方法进行学习效果欠佳.本文提出一种卷积优化的变分自编码器，用多个可变层数的卷积网络预处理图像数据.每个卷积网络设置了不同的参数处理输入数据，再将不同层卷积结果拼接后，作为变分自编码器的输入.在变分自编码模型中增加一个类别编码器，用于计算每个样本的类别分布和原样本集中类别分布的差异，实现聚类.实验证明，本文提出的卷积优化方法相较于无优化的变分自编码器在聚类准确率上得到较大提高，生成图像的质量得到了改善，各类别生成样本在边缘及形状等方面的多样性也都有不同程度的增加.

Clustering Method Based on VAE with Convolution Optimization

The traditional Variational AutoEncoder (VAE) takes the flattened sample as input data directly. When the sample is image data, the effect of learning by this method is weakly. In this study, VAE with the convolution optimization is proposed to preprocess image data with multiple convolution networks of variable layers. Each convolution network sets different parameters to process the input data, then splices the results of different layers as the input of VAE. Clustering is implemented through the distance between the category label distribution of original dataset and the category distribution of each sample is calculated by adding a category encoder. The experimental results show that the convolution optimization method proposed in this study improves the clustering accuracy compared with the non-optimal VAE, increases the quality of the generated image and the diversity of the generated samples in the edge and shape.