Questions tagged [variational-autoencoder]

For questions related to variational auto-encoders (VAEs). The first VAE was proposed in "Auto-Encoding Variational Bayes" (2013) by Diederik P. Kingma and Max Welling. There are several other VAEs, for example, the conditional VAE.

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Why can't VAE do sequence to sequence name generation?

I'm working on research in this sector where my supervisor wants to do cannonicalization of name data using VAEs, but I don't think it's possible to do, but I don't know explicitly how to show it ...
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1answer
292 views

What is the input for the prior model of VQ-VAE?

I'm trying to implement the VQ-VAE model. In there, a continuous variable $x$ is encoded in an array $z$ of discrete latent variables $z_i$ that are mapped each to an embedding vector $e_i$. These ...
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41 views

In variational auto-encoders, what should the dimension of the mean and variance of the latent distribution and the latent vector be?

I'm having a problem to understand the needed dimensions of an VAE, especially for mu, logvar and z layer. Let's say I have an input of 512x512, 1 color channel (CT images), batch size 32. Then my ...
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27 views

Compressing Parameters of an Response System

I have an input-output system, which is fully determined by 256 parameters, of which I know a significant amount are of less importance to the input-output pattern. The data I have is some (64k in ...
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0answers
167 views

Why would a VAE train much better with batch sizes closer to 1 over batch size of 100+?

I've been training a VAE to reconstruct human names and when I train it on a batch size of 100+ after about 5 hours of training it tends to just output the same thing regardless of the input and I'm ...
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1answer
167 views

What is the impact of scaling the KL divergence and reconstruction loss in the VAE objective function?

Variational autoencoders have two components in their loss function. The first component is the reconstruction loss, which for image data, is the pixel-wise difference between the input image and ...
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16 views

Does bottleneck size matter in Disentangled Variational Autoencoders?

I suppose that picking an appropriate size for the bottleneck in Autoencoders is neither a trivial nor an intuitive task. After watching this video about VAEs, I've been wondering: Do disentangled ...
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47 views

Why does the ELBO come to a steady state and the latent space shrinks?

I'm trying to train a VAE using a graph dataset. However, my latent space shrinks epoch by epoch. Meanwhile, my ELBO plot comes to a steady state after a few epochs. I tried to play around with ...
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141 views

Are there any general tips for troubleshooting a VAE when apparently it is not learning?

I am trying to train a VAE for anomaly detection. I chose one architecture from this Github repository and I adjusted the input and output to match what I need. In my case, the input (and hence the ...
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80 views

Why does the VAE using a KL-divergence with a non-standard mean does not produce good images?

I know I can make a VAE do generation with a mean of 0 and std-dev of 1. I tested it with the following loss function: ...
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26 views

variational auto encoder loss goes down but does not reconstruct input. out of debugging ideas

My variational autoencoder seems to work for MNIST, but fails on slightly "harder" data. By "fails" I mean there are at least two apparent problems: Very poor reconstruction, for ...
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41 views

VAE giving near zero output when latent space dimension is large

I'm training a VAE to reconstruct some input (channels picked up by some MIMO BS for context) and I ran an experiment on the training set to see how the performance improves with the latent space ...
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64 views

Hairstyle Virtual Try On

I want to help people with cancer who are under chemotherapy, and generally people who have lost their hair to Virtually Try-On Toupees/Wigs on their head. VTO must support both the frontal and side ...
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43 views

How can I reconstruct sparse one-hot encodings using an RBM?

I am currently working with a categorical-binary RBM, where there are 50 categorical visible units and 25 binary hidden units. The categorical visible units are expressed in one-hot encoding format, ...
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1answer
47 views

How to construct input dependent convolutional filter?

I am constructing a convolutional variational autoencoder for images, starting out with mnist digits. Typically I would specify convolutional layers in the following way: ...
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84 views

Is Gradient Descent algorithm a part of Calculus of Variations?

As in https://en.wikipedia.org/wiki/Calculus_of_variations The calculus of variations is a field of mathematical analysis that uses variations, which are small changes in functions and ...
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38 views

What's the difference between semi-supervised VAEs and conditional VAEs?

Can someone explain the difference? I'm assuming the difference is just that the neural nets representing the encoder and decoder are trained in a semi-supervised manner in semi-supervised VAE, which ...
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61 views

Why is my variational auto-encoder generating random noise?

This is my first variational autoencoder. Background info: I am using the MNIST digits dataset. The model is created and trained in PyTorch. The model is able to get a reasonably low loss, but the ...
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29 views

Reduce same sample distance in VAE encodings

I'm working on a beta VAE model learning a latent representation used as a similarity metric for image registration. One of the main problems I'm facing is that the encoder + sampler output doesn't ...
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1answer
18 views

In variational autoencoders, what does p(x|z) mean?

If $x \sim \mathcal{N}(\mu,\,\sigma^{2})$, then it is a continuous variable, and therefore $P(x) = 0$ for any x. One can only consider things like $P(x<X)$ to get a probability greater than 0. So ...
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16 views

How can I avoid a negative covariance value in my variational auto-encoder?

I am implementing the variational auto-encoder from this paper (Section 3.1): https://arxiv.org/pdf/2004.06271.pdf My KL loss function is giving NaN values because some of my covariances are negative. ...
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16 views

In VQ-VAE code what does this line of code signify?

The VQ-VAE implimentation:https://colab.research.google.com/github/zalandoresearch/pytorch-vq-vae/blob/master/vq-vae.ipynb ...
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Is there an effective way of obtaining the topic distribution for a given document from a VAE-LDA?

Is there an effective way of obtaining the topic distribution for a given document from a Variational AutoEncoder Latent Dirichlet Allocation (VAE-LDA)? Most existing public VAE-LDA codebases seem to ...
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10 views

Parametrizing non-analytical functions using generative models

My questions centers around what method is best to use parametrize a response function for an experiment. We are currently using ab initio simulation to model our experiment's response. Unfortunately, ...
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45 views

How to compare multiple one-class variational autoencoders?

I have trained multiple one-class vanilla variational autoencoders that each learn the distribution of one class and have the same architecture. The classes are mostly discrete, but there are several ...
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28 views

Why using Aux() to implement VAE_1?

I have a bit of difficulties working on the article :https://arxiv.org/pdf/2009.07047v1.pdf. I want to implement the VAE_1, but it used VAE-GAN. I am familiar with ...
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46 views

VAE KL divergence loss decreases really fast

I am new to training VAEs and I am using it on some 16x16 images, that contains some images from a physics experiment with one or 2 events i.e. the images are mainly black, except for one or 2 regions ...
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254 views

Conditional Variational Autoencoder - NON Image Data

First I would like to expand an issue I've been dealing with way too long: Creating a conditional Variational Autoencoder with continuous variables in non-image data ( more specifically, time series). ...