All Questions
Tagged with latent-variable generative-model
9 questions
3
votes
1
answer
133
views
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 ...
CommunityBot
- 1
1
vote
0
answers
123
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=...
- 1,217
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-...
- 111
2
votes
1
answer
185
views
how the GAN architecture maintain similar images close in the latent space?
I am learning about generative models, and I don't quite understand how the GAN architecture can maintain similar generated images close in the latent space. For example, an autoencoder and a ...
- 33.3k
5
votes
2
answers
4k
views
How to generate new data given a trained VAE - sample from the learned latent space or from multivariate Gaussian?
To generate synthetic dataset using a trained VAE, there is confusion between two approaches:
Use learned latent space: z = mu + (eps * log_var) to generate (...
- 316
1
vote
1
answer
131
views
What is meant by degrees of freedom of latent variables?
...Designing such a likelihood function is typically challenging; however, we observe that features like spectrogram are effective when latent variables have limited degrees of freedom. This motivates ...
- 1,836
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 ...
- 121
3
votes
2
answers
238
views
Do we also need to model a probability distribution for the decoder of a VAE?
I'm working on understanding VAEs, mostly through video lectures of Stanford cs231n, in particular lecture 13 tackles on this topic and I think I have a good theoretical grasp.
However, when looking ...
CommunityBot
- 1
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 ...
- 41.4k