Bayesian GAN - Yunus Saatchi
We all know what GANs are. The stories of the tough game between the Generator and Discriminator which ends in neither of them winning - but instead reaching a mutual agreement or a compromise(the Nash Equilibrium) have been going around since 2014. Along with the story, the disadvantages of GAN have also gained popularity. Let's address one of them for time being: Mode Collapse. Mode Collapse: Let's consider a 2D example - as it is easier to visualize a 2D plane. Suppose the following is your data: There is a 2D plane of size 400x100. However, data of your interest is restricted to 3 small clusters compared to the whole plane. Basically you want the GAN to generate only points that belong in the red clusters. Real Data You design an ordinary GAN to mimic your data given the 2D space. However, your GAN is able to capture only partial data. It is not able to generate the data present at the top left corner. Generated Data This is referred to as Mode Col...