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Clarification on the training objective of denoising diffusion models

I'm reading the Denoising Diffusion Probabilistic Models paper (Ho et al. 2020). And I am puzzled about the training objective. I understood (I think) the trick regarding the reparametrization of the ...
user3903647's user avatar
2 votes
0 answers
36 views

Why do hypercube latent spaces perform poorer than Gaussian latent spaces in generative neural networks?

I have a quick question regarding the use of different latent spaces to represent a distribution. Why is it that a Gaussian is usually used to represent the latent space of the generative model ...
AlphaBetaGamma96's user avatar
1 vote
0 answers
125 views

Are diffusion models still beneficial in highly compressed latent spaces?

Consider for example the MNIST dataset. When we apply diffusion to the pixel space, the image slowly becomes more and more noisy until white noise has been reached (like below). In the last step (t=...
Thomas Wagenaar's user avatar
1 vote
0 answers
93 views

Pointers to (deep) latent variable models that admit analytical approximations

I am aware that there is a plethora of deep generative models out there (e.g. variational autoencoders (VAE), GANs) that can model high-dimensional data as the images of latent variables under a non-...
ngiann's user avatar
  • 111
1 vote
0 answers
75 views

What kind of distributions can be used to model discrete latent variables?

If we take the vanilla variational auto-encoder (VAE), we $p(z)$ is a Gaussian distribution with zero mean and unit variance and we approximate $p(z|x) \approx q(z|x)$ to be a Gaussian distribution as ...
piccolo's user avatar
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