  Autoencoders receive latent models of input data. It was shown in recent
works that they also estimate probability density functions of the input. This
fact makes using the Bayesian decision theory possible. If we obtain latent
models of input data for each class or for some points in the space of
parameters in a parameter estimation task, we are able to estimate likelihood
functions for those classes or points in parameter space. We show how the set
of autoencoders solves the recognition problem. Each autoencoder describes its
own model or context, a latent vector that presents input data in the latent
space may be called treatment in its context. Sharing latent spaces of
autoencoders gives a very important property that is the ability to separate
treatment and context where the input information is treated through the set of
autoencoders. There are two remarkable and most valuable results of this work:
a mechanism that shows a possible way of forming abstract concepts and a way of
reducing dataset's size during training. These results are confirmed by tests
presented in the article.
