[theCricketer]

> Auto-encoders are overplayed, mostly because they're a pretty easy intro ML project.

I think you mean "normal" autoencoders, like denoising autoencoders or the identity autoencoder that are used for feature learning. Note that variational autoencoders are not really autoencoders in that sense. They are called “autoencoders” only because the final training objective that derives from the probabilistic setup does have an encoder and a decoder, and resembles a traditional autoencoder.

Traditional autoencoders are the common intro projects used for representation learning and to bootstrap other networks, not variational autoencoders.