Previously, I discussed in-depth the vanishing gradient problem in GANs and how it leads to unstable and ineffective learning. I then introduced the Wasserstein GAN, one of the popular solutions to this problem. While most research efforts into GANs have been in line with this goal of improving training stability, the game doesn’t end here. There … Continue reading Improving the GAN Part 2: Conditional and Controllable generation