Hands-On Generative Adversarial Networks with Keras

Working with discrete inputs

Use an embedding layer with discrete inputs.

An embedding layer learns a projection from discrete inputs into a dense vector of fixed size. This has several advantages, including the following:

• The network learns the best embedding given the task at hand.
• The dimensionality of the discrete input can be reduced.

Learn discrete input upsampling to match image channel size.

It is believed that it is better to keep the embedding dimensionality low and upsample it to match the image channel size at the layer at hand.

Concatenate discrete inputs with image channels.

In theory, concatenating discrete inputs with images over the channel's dimension and then applying a convolution or dense layer is a superset of adding ...