I’d like to know what the advantage is over KL divergence. It seems like the important idea is symmetry? Not clear to me why that matters; I’d love to know what application this is used for.
Easy conversion into a distance metric is hugely valuable to making the property amenable to KNN-based dimensionality reduction algos (and I'm sure other things I don't understand, as a non-mathematician)
Here's a library that the creator of UMAP provides (UMAP being a workhorse of dimensional reduction algos), for doing approx nearest neighbor search: https://pynndescent.readthedocs.io/en/latest/api.html#pynnde...
I don’t think GANs are used much now in comparison to diffusion models, but as recently as a few years ago they were the standard way to make fake data, a la “this face does not exist”