Neural Network Programming with TensorFlow

Autoencoders

One way to convert a generative to a discriminative problem can be by learning the mapping from the input space itself. For example, we want to learn an identity map that, for each image x, would ideally predict the same image, namely, x = f(x), where f is the predictive model.

This model may not be of use in its current form, but from this, we can create a generative model.

Here, we create a model formed of two main components: an encoder model q(h|x) that maps the input to another space, which is referred to as hidden or the latent space represented by h, and a decoder model q(x|h) that learns the opposite mapping from the hidden input space.

These components--encoder and decoder--are connected together to create an end-to-end ...