GANs were fun though. :)

Though if you can rephrase the problem into a diffusion it seems to be prefered these days. (Less prone to mode collapse)

Gan is famously used for generative usecases, but has wide uses for creating useful latent spaces with limited data, and show up in few-shot-learning-papers. (Im actually not that up to speed on the state of art in few-shot so mabie they have something clever that replace it)

Yes you can just concentrate on the latest models but if you want a better grounding in the field some understanding of the past is important. In particular reusing ideas from the past in a new way and/or with better software/hardware/datasets is a common source of new developments!

Also there are libraries that abstract away most if not all the things, so you don't have to know everything

In practice, when it comes down to code, even without higher-level libraries, it is surprisingly simple, concise and intuitive.

Most of the math elements used have quite straightforward properties and utility, but of course if you combine them all together into big expressions with lots of single-character variables, it's really hard to understand for everyone. You kind of need to learn to squint your eyes and understand the basic building-blocks that the maths represent, but that shouldn't be necessary if it wasn't obfuscated like this.