GAN is not a architecture its a training method. As the models themselves change underneath, GAN remain relevant. (Just as you see autoencoder still being used

GAN is not a architecture its a training method. As the models themselves change underneath, GAN remain relevant. (Just as you see autoencoder still being used as a term in new published works, which is even older.) 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)