  As a general-purpose generative model architecture, VAE has been widely used
in the field of image and natural language processing. VAE maps high
dimensional sample data into continuous latent variables with unsupervised
learning. Sampling in the latent variable space of the feature, VAE can
construct new image or text data. As a general-purpose generation model, the
vanilla VAE can not fit well with various data sets and neural networks with
different structures. Because of the need to balance the accuracy of
reconstruction and the convenience of latent variable sampling in the training
process, VAE often has problems known as "posterior collapse". images
reconstructed by VAE are also often blurred. In this paper, we analyze the main
cause of these problem, which is the lack of mutual information between the
sample variable and the latent feature variable during the training process. To
maintain mutual information in model training, we propose to use the auxiliary
softmax multi-classification network structure to improve the training effect
of VAE, named VAE-AS. We use MNIST and Omniglot data sets to test the VAE-AS
model. Based on the test results, It can be show that VAE-AS has obvious
effects on the mutual information adjusting and solving the posterior collapse
problem.
