r/MachineLearning - [D] Generative Adversarial Networks - The Story So Far

Well, as far as I can tell, the world of deep learning works very differently from regular computer science. If you've seen that popular xkcd comic about machine learning being a pile of linear algebra that we stir up and experiment with, you'll know what I mean. The idea of using the 1-Wasserstein distance instead of an approximation of the Jensen-Shanon divergence (the WGAN model) is "groundbreaking" for two reasons: It produced images that simply had a better quality overall. This was probably the most significant factor. Hypothetically, you could come up with your own weird new distance measure that has no rigorous mathematical justification, and if it beats state of the art by a non-trivial margin, it would be considered just as groundbreaking.