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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16 views

How does the VAE learn p(x,z)?

I found the following paragraph from An Introduction to Variational Autoencoders sounds relevant, but I am not fully understanding it. A VAE learns stochastic mappings between an observed $\mathbf{x}$...
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32 views

What is the most suitable measure of the distance between two VAE's latent spaces?

The problem I'm trying to solve is as follows. I have two separate domains, where inputs do not have the same dimensions. However, I want to create a common feature space between both domains using ...
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40 views

What are the roles of the prior $\mathrm{p}(\mathbf{z})$ in a VAE?

I know the encoder is variational posterior $q_{\phi}(\mathbf{z} \mid \mathbf{x})$. I also know that the decoder represents the likelihood: $p_{\theta}(\mathbf{x} \mid \mathbf{z})$. My question is ...
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1answer
63 views

How to determine the quality of synthetic data?

I'm working on a VAE model to produce synthetic data of X-Ray diffraction spectrums. I try to figure out how I can measure the quality of the spectrums. The goal would be to produce synthetic data ...
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33 views

variational autoencoder - decoder output for images

Following the standard setup/notation for a VAE, let $z$ denote the latent variables, $q$ as the encoder, $p$ as the decoder, and $x$ as the label. Let the objective be to maximize the ELBO, where a ...
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11 views

Building Gaussian Mixture VAE using pytorch

I am trying to implement GMM-VAE model using torch. Basically I have a problem implementing for instance equation (1c) where the posterior distribution $p(x|z,w)$ is a Gaussian with the mean and ...
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1answer
27 views

Are some low dimensional distributions known to be hard to model with VAEs?

I am trying to implement a toy VAE project. My goal is to use a VAE to model the moon dataset from scikit-learn, with an extra constant (but noisy) z-dimension. To this end I use an approximate ...
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13 views

Tensorflow Probability Implementation of Automatic Differentiation Variational Inference with Mixtures

In this paper, the authors suggest using the following loss instead of the traditional ELBO in order to train what basically is a Variational Autoencoder with a Gaussian Mixture Model instead of a ...
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20 views

Why is the prior on the latent variable standard gaussian in VAE?

While training a standard VAE, we assume that the prior on the latent variable Z is the standard gaussian and we use KL divergence to push the posterior as close as possible to the standard gaussian. ...
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17 views

Types of decoder parametrizations in VAE for continuous data

I'm wondering what are the different choices of parametrizations available for the decoder in a variational autoencoder. If the data is discrete, you can just output probabilities for each class, so ...
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1answer
31 views

Are mean and standard deviation in variational autoencoders unique?

In general, if I have a collection of data then mean(Expectation) and standard deviation are calculated as follows $$\text{mean } = \mu = \mathbb{E}[X] = \sum\limits_{i = 1}^n p_ix_i $$ $$\text{...
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96 views

Can I use the VAE for dimensionality reduction?

I'm doing a project that uses a clustering algorithm for the facial expression classification task. So, I use the output of the encoder in the VAE autoencoder for dimensionality reduction. However, I ...
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1answer
41 views

Is there a performace benefits using VAE-GAN instead of just GAN?

I have read that when using VAE-GANs, first what happens is the VAE's encoder encodes some image to another encoded image, which from GAN's point of view is considered a noise, and then the GAN part ...
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24 views

Problems while transforming a 2D Variational Autoencoder into a 1D Version

I am trying to addapt the Keras variational autoencoder (VAE) here from a 2-D input/output (matrix of a picture) to a 1-D input/output (just a vector). I thought this would be a fearly easy task, but ...
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27 views

Weird KL divergence behaviour

I'm training a complex model for motion prediction using a VAE, however the KL divergence has a very strange behavior. A scheleton of the network is the following: At the end my network compute the ...
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38 views

Doubts on how this Dimensions are handled (Tensorflow) and how mean and standard deviation are extracted [closed]

I'm following the introductory MIT Deep Learning course on Youtube and i've been stuck for a day now on this piece of code: ...
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1answer
77 views

What does the approximate posterior on latent variables, $q_\phi(z|x)$, tend to when optimising VAE's

The ELBO objective is described as follows $$ ELBO(\phi,\theta) = E_{q_\phi(z|x)}[log p_\theta (x|z)] - KL[q_\phi (z|x)||p(z)] $$ This form of ELBO includes a regularisation term in the form of the ...
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34 views

Are there architectures to generate pictures from four labels? (VAEs, GANs)

I want to try something with image creation via NNs. I have come across Variational Autoencoders and Generative Adversarial Networks as possible solutions but have only found image creatinon with ...
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103 views

In variational autoencoders, why do people use MSE for the loss?

In VAEs, we try to maximize the ELBO $\mathbb(E_q log\ p(x|z) + D_{KL}(q(z|x), p(z))$), but I see that many implement the first term as MSE of the image and it's reconstruction. Is this mathematically ...
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76 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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20 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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29 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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20 views

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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105 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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319 views

Why is "-0.5 * torch.sum(1 + sigma - mu.pow(2) - sigma.exp())" in Pytorch equivalent to the KL?

I found this code for the loss function of a VAE: -0.5 * torch.sum(1 + sigma - mu.pow(2) - sigma.exp()) From this link: https://debuggercafe.com/getting-started-...
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1answer
81 views

How do you calculate KL divergence on a three-dimensional space for a Variational Autoencoder?

I'm trying to implement a variational auto-encoder (as seen in Section 3.1 here: https://arxiv.org/pdf/2004.06271.pdf). It differs from a traditional VAE because it encodes its input images to three-...
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35 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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73 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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1answer
1k views

What are the fundamental differences between VAE and GAN for image generation?

Starting from my own understanding, and scoped to the purpose of image generation, I'm well aware of the major architectural differences: A GAN's generator samples from a relatively low dimensional ...
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11 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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68 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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97 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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1answer
41 views

What do they mean by "contradictory loss"?

In page 4 of the paper https://arxiv.org/pdf/2009.07047v1.pdf, it says the encoder $E_{R,X}$ of $VAE_1$ tries to fool the discriminator with a contradictory loss to ensure that $R$ and $X$ are mapped ...
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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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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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390 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
62 views

How does implementation of VAE's objective function equate to ELBO?

For a lot of VAE implementations I've seen in code, it's not really obvious to me how it equates to ELBO. $$L(X)=H(Q)-H(Q:P(X,Z))=\sum_ZQ(Z)logP(Z,X)-\sum_ZQ(Z)log(Q(Z))$$ The above is the definition ...
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59 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
408 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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1answer
81 views

How many types of variational auto-encoders are there?

I have been studying about auto-encoders and variational auto-encoders. I would like to know how many variants of VAEs are there today. If there are many variants, can they be used for feature ...
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60 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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1answer
128 views

What does the notation $\mathcal{N}(z; \mu, \sigma)$ stand for in statistics?

I know that the notation $\mathcal{N}(\mu, \sigma)$ stands for a normal distribution. But I'm reading the book "An Introduction to Variational Autoencoders" and in it, there is this notation:...
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1answer
48 views

What is the main contribution of the paper Disentangling by Factorising?

Considering the paper Disentangling by Factorising, in addition to introducing a new model for Disentangled Representation Learning, FactorVAE (see figure), what is the main theoretical contribution ...
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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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1answer
132 views

Can a variational auto-encoder learn images composed of random noise at each pixel (each drawn from the same distribution)?

Can a variational auto-encoder (VAE) learn images whose pixels have been generated from a Gaussian distribution (e.g. $N(0, 1)$), i.e. each pixel is a sample from $N(0, 1)$? My gut feeling says no, ...
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1answer
275 views

Why does the variational auto-encoder use the reconstruction loss?

VAE is trained to reduce the following two losses. KL divergence between inferred latent distribution and Gaussian. the reconstruction loss I understand that the first one regularizes VAE to get ...
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1answer
422 views

Why do we regularize the variational autoencoder with a normal distribution?

When we define the loss function of a variational autoencoder (VAE), we add the Kullback-Leibler divergence between the sample taken according to a normal distribution of parameters: $$ N(\mu,\sigma)...
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1answer
253 views

Why does KL divergence not satisfy the triangle inequality?

$D_{KL}=\sum_i p(x_i)log(p(x_i)/q(x_i)$ Also can't you make it satisfy the triangle inequality by taking the absolute value of the information at every point?
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144 views

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
74 views

In this VAE formula, why do $p$ and $q$ have the same parameters?

In $$\log p_{\theta}(x^1,...,x^N)=D_{KL}(q_{\theta}(z|x^i)||p_{\phi}(z|x^i))+\mathbb{L}(\phi,\theta;x^i),$$ why does $p(x^1,...,x^N)$ and $q(z|x^i)$ have the same parameter $\theta?$ Given that $p$ is ...