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Rectifying Mode Collapse in GANs

GANs or Generative Adversarial Models have done exceptionally good in the field of generative models. It involves two adversarial networks Generator and Discriminator compete against one another. They have a lot of applications which involve data generation, image-to-image translation, text-to-image translation etc. However GANs have the anomaly of mode collapse. The generator over-optimizes for a specific discriminator with each iteration, and the discriminator never learns how to get out of the trap. As a result, the generators switch between a small number of output types. In this paper we are going to compare the different ways in which the issue of mode collapse is tackled while implementing GANs.