  Generative adversarial networks (GANs) are capable of generating strikingly
realistic samples but state-of-the-art GANs can be extremely computationally
expensive to train. In this paper, we propose the fused propagation (FusedProp)
algorithm which can be used to efficiently train the discriminator and the
generator of common GANs simultaneously using only one forward and one backward
propagation. We show that FusedProp achieves 1.49 times the training speed
compared to the conventional training of GANs, although further studies are
required to improve its stability. By reporting our preliminary results and
open-sourcing our implementation, we hope to accelerate future research on the
training of GANs.
