[bcheung]

I'd be curious to see how different levels of quantization affect the image. From the paper it looks like the quantization is applied at the latent feature space. I wonder if it has similar effects like the celebrity GAN's we have seen where interpolating in the latent space results in morphing from one face to another. Could be funny when compression doesn't result in something blocky or distorted, but replacing objects with other objects that look similar to them.

This seems to be for static images, but this gets me wondering if an RNN can be used and have better motion prediction that other current "hard coded" solutions.

Also, the more specific the domain, the better the compression, since it can specialize. I'm wondering about the practical applications of this. Do we have different baselines that can be used for different use cases?

[make3]

it also means that you could get a meaningful average of two images by averaging their compressed form (aka latent state z), and decoding, just like with the celebrities :)