[D] Why is DiscoGAN better at geometrical transformation when compared to CycleGAN ?
CycleGAN and DiscoGAN are very similar in their functionality and seem to be concurrent works. The loss function of CycleGAN is L1 loss while DiscoGAN uses MSE. CycleGAN has an additional identity loss function.
While CycleGAN produces impressive results on horse2zebra, it seems to fail at the task of cat2dog (geometric transformation). DiscoGAN, on the other hand, is able to perform the task of Handbags2Shoes.
TL;DR: What makes DiscoGAN perform the geometrical transformation better than CycleGAN ? Is it the network architecture or the MSE loss function or is there is a secret sauce ?