GANs can be useful as alternative/additional loss functions. E.g. the original pix2pix paper: https://arxiv.org/abs/1611.07004 Here, they have pairs (X, Y) available, so they could just train this as a regression task directly. However, they found better results using L1 loss plus a GAN loss.

Keep in mind that using something like squared error loss has a ton of assumptions underlying it (if you interpret training as maximum likelihood estimation) such as outputs being conditionally independent and following a Gaussian distribution. A GAN discriminator can represent a more complex/more appropriate loss function.

Note, I'm not saying that a lot of these papers might not add anything of value, but there are reasons to use GANs even if you have known input-output pairs.

It doesn't really matter whether or not there are people *working* on other stuff -- the two terms are different by definition.

You're right, of course. I think I just misunderstood what you were getting at with your answer when I made that comment.

There are many ML models that are not neural networks.

I like how every single reference is either 2016 or newer, OR Schmidhuber.