What is a GAN?
The ability of GANs to learn high-dimensional, complex data distributions have made them very popular with researchers in recent years. Between 2016, when they were first proposed by Ian Goodfellow, up to 2019, we have more than 40,000 research papers related to GANs. This is in the space of just three years!
The applications of GANs include creating images, videos, music, and even natural languages. They have been employed in tasks like image-to-image translation, image super resolution, drug discovery, and even next-frame prediction in video.
The key idea of GAN can be easily understood by considering it analogous to "art forgery," which is the process of creating works of art that are falsely credited to other usually more famous artists. GANs train two neural nets simultaneously. The generator G(Z) is the one that makes the forgery, and the discriminator D(Y) is the one that can judge how realistic the reproductions are, based on its observations...
