Generative Adversarial Networks Projects

Losses for Stage-II of StackGAN

Similar to any other GAN, the generator G and the discriminator D in the Stack-II GAN can also be trained by maximizing the loss for the discriminator and minimizing the loss for the generator network.

The generator loss  can be represented as follows:

                                             

The preceding equation is pretty self-explanatory. It represents the loss function for the discriminator network, in which both networks are conditioned on the text embeddings. One major difference is that the generator ...