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[D] How to use VAE’s encoded mu and sigma with respect to user-given z?

My understanding of VAE is that unlike Autoencoders, it does not directly give you a discrete encoding (latent code vectors n-dim) instead, it gives you both mu and sigma (n- dim mean vectors and n-dim standard deviation vectors). Then you have epsilon which you use to sample from a normal distribution with mu and sigma to create z. When combining mu, sigma and epsilon, you get z which is the one decoded by the VAE’s decoder. z is basically the main encoding.

Say my z, mu, sigma are of n-dimension like 10 (10-dim z, mu, sigma). I enable the user to have a free picking/giving me numbers 10 vectors [-a, a], say a = 5. So the user is free to pick 10 vectors between -5, 5.

This becomes my z that is decoded by my decoder to generate a new image.

[Main problem]

My VAE is trained on a dataset of apparel. Now, if I run my VAE’s encoder on each of the data in the dataset, I’d get a mu and sigma for each (not sure if this is still correct).

Using the z given by the user, how do I find the most similar from the dataset using VAE’s encoding of only mu and sigma?

My thinking is to generate z using mu and sigma generated by VAE’S encoder but in order to generate z, I still need to sample from a distribution using epsilon in which makes it non-discrete w.r.t user-generated z. This adds randomness to it so I am not sure how would I use the encoded z to find the nearest to user-generated z.

submitted by /u/sarmientoj24
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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.