Classic Autoencoders and variational autoencoders are used to learn complex
data distributions, that are built on standard function approximators, such as
neural networks, which can be trained by stochastic gradient descent methods.
Especially, VAE has shown promise on a lot of complex task. In this paper, a
new autoencoder model - classification supervised autoencoder (CSAE) based on
predefined evenly-distributed class centroids (PEDCC) is proposed. To carry out
the supervised learning for autoencoder, we use PEDCC of latent variables to
train the network to ensure the maximization of inter-class distance and the
minimization of inner-class distance. Instead of learning mean/variance of
latent variables distribution and taking reparameterization of VAE, latent
variables of CSAE are directly used to classify and as input of decoder. In
addition, a new loss function is proposed to combine the loss function of
classification, the loss function of image codec error and the loss function
for enhancing subjective quality of decoded image. Based on the basic structure
of the universal autoencoder, we realized the comprehensive optimal results of
encoding, decoding and classification, and good model generalization
performance at the same time. Theoretical advantages are reflected in
experimental results.Comment: 17 pages,9 figures, 5 table
