Does anyone know of papers on creating human-interpretable latent spaces with auto-encoders?
An example of the systems I have in mind would be a NN generating face images from a latent space, designed such that dimension 0 encodes skin tone, dimension 1 encodes hair colour etc.
Will be doing my own literature search but if anyone knows the area some pointers to papers or search terms would be very helpful!
Question:
Does anyone know of papers on creating human-interpretable latent spaces with auto-encoders?
An example of the systems I have in mind would be a NN generating face images from a latent space, designed such that dimension 0 encodes skin tone, dimension 1 encodes hair colour etc.
Will be doing my own literature search but if anyone knows the area some pointers to papers or search terms would be very helpful!
There is definitely something out there, just can’t recall the name. A keyword you might want to look for is “disentangled representations”.
One start would be the beta-VAE paper https://openreview.net/forum?id=Sy2fzU9gl
Cheers!