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[Discussion] Methods to use alternative form of reconstruction objective for VAE than pixelwise error

I am currently working on a project which involves improving the reconstruction capability of the VAE perceptually. Since the basic VAE objective uses the pixelwise error for the reconstruction part, the generated images have a peculiar blurry characteristic which makes them perceptually unreal. I did keyword searches on Scholar and ResearchGate, but was not able to find works that replace this pixelwise metric with something more appropriate for images.

The closest I got was with the paper titled “Autoencoding beyond pixels using a learned similarity metric” https://arxiv.org/pdf/1512.09300.pdf. This is a great piece of work and I find the idea of combining the GAN discriminator with the VAE superb.

In my search, I also found the flow based papers such as GLOW and RealNVP. But these use the reversible operations because of which, the posterior probability can be easily calculated since it is a deterministic function of the prior probability. I am actually looking for the variational inference generative models which simply use a different form of reconstruction objective for better perceptual results.

I kindly request all the fellow redditors to please provide me with works that you are aware of. It would be a great help. Thanking you.

Best regards,

akanimax

submitted by /u/akanimax
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Toronto AI is a social and collaborative hub to unite AI innovators of Toronto and surrounding areas. We explore AI technologies in digital art and music, healthcare, marketing, fintech, vr, robotics and more. Toronto AI was founded by Dave MacDonald and Patrick O'Mara.