Discussion: β-VAE vs GAN

[donovanr]

Issue summary

discuss new model re old one(s)

Details

There are a lot of interesting things to say about the beta-VAE as compared to the GAN used in both previous manuscripts (arXiv1705.00092 for the 2D version and bioRxiv 238378 for the 3D version). In particular, the ability to order the dimensions within the latent space rationally with respect to the amount of variance explained is a game changer for making the output of the autoencoder biologically interpretable. This really needs to be emphasized in the discussion as being useful both for the generative applications and for the dimensionality reduction applications.

Both the beta-VAE and the adversarial network end up enforcing a Gaussian prior in all latent space dimensions, but my understanding is that this is through very different mechanisms: the beta-VAE simply includes the KL divergence as part of the loss function (simple and elegant) while the GAN was trying to make the reconstructions similar between real images and constructed images drawn from an n-dimensional Gaussian (works, but is kind of fussy and ad hoc). I suspect that this also puts the generated images drawn from the beta-VAE on a much firmer statistical footing with respect to drawing conclusions about differences between cell populations. It would be nice to discuss this a little bit in the discussion, particularly in the context of facilitating the use of this for cell biological research. In more personal terms, Greg was very excited about the GANs a couple of years ago but then soured on them to the extent that in this manuscript he didn’t really even want to acknowledge the profound difference between the prior art and this one. He went on some kind of intellectual journey that convinced him that GANs should be abandoned and beta-VAEs embraced instead. It would be a service to the community to communicate what was learned that led to this conclusion.

TODO

• [ ] manuscript text

[tknijnen]

Revised discussion, left placeholder for this issue.