<p>You can indeed put a variational autoencoder (VAE) in front in order to generate the initial distribution z (see <a href="https://arxiv.org/abs/1512.09300" rel="nofollow noreferrer">paper</a>). </p>

<p>If you are interested in the topic I can recommend the this <a href="https://www.kadenze.com/courses/creative-applications-of-deep-learning-with-tensorflow/info" rel="nofollow noreferrer">course</a> at Kadenze.</p>
GAN with not a random input
<p>I'm very interested in GAN those times.</p>

<p>I coded one for MNIST with the following structure :
Generator model
Discriminator model
Gen + Dis model</p>

<p>Generator model generate batches of image from random distribution.
Discrimator is trained over it and real images.
Then Discriminator is freeze in Gen+Dis model and Generator trained. (With the frozen Discriminator who says if the generator is good or not)</p>

<p>Now, imagine I don't want to feed my generator with a random distribution but with images. (For upscaling for example, or generate an real image from a draw)</p>

<p>Do I need to change something in it ?
(Except the conv model who will be more complex)
Should I continue to use the binary_crossentropy as loss function ?</p>

<p>Thanks you very much!</p>