Different latent variables learning in variational autoencoder

Unsupervised learning is a good neural network training way. However, the unsupervised learning algorithm is rare. The generative model is an interesting algorithm which can generate the similar data as the sample data by building a probabilistic model of the input data, and it can be used for unsupervised learning. Variational autoencoder is a typical generative model which is different from common autoencoder that a probabilistic parameter layer follows the hidden layer. Some new data can be reconstructed according to probabilistic model parameters. The probabilistic model parameter is the latent variable. In this paper, we want to do some research to test the data reconstruct effect of the variational autoencoder by different latent variables. According to the simulation, the more latent variables the more style of the sample is.