Is Maximum Likelihood Useful for Representation Learning?

A few weeks ago at the DALI Theory of GANs workshop we had a great discussion about what GANs are even useful for. Pretty much everybody agreed that generating random images from a model is not really our goal. We either want to use GANs to train conditional probabilistic models (like we do for image super-resolution or speech synthesis, or something along those lines), or as a means of unsupervised representation learning. Indeed, many papers examine the latent space representations that GANs learn. But the elephant in the room is that nobody really agrees on what unsupervised representation learning really means, and why any GAN variant should be any better or worse at it than others, whether GANs or VAEs are better for that.