I was going through this paper on Towards Text Generation with Adversarially Learned Neural Outlines and it states why the VAEs are hard to train for text generation due to this problem. The paper states the model ends up relying solely on the auto-regressive properties of the decoder while ignoring the latent variables, which become uninformative. please simplify and explain the problem in a lucid way.

I know that autoencoders can be used to generate new data. From what I could understand.. The autoencoder uses the original distribution X to learn a random gaussian distribution described by mean and sigma. Then the decoder will use the mean and sigma parameters to randomly sample a new parameter which will be close to X. My question is that let's say I have information about two features X and Y.Then can I use the variational auto encoder to generate …

I have time series data, with many features. I would like to reduce the dimentionality by using LSTM VAE. Does anybody know an example code or a reference to guide me to impolement it? Both Pytorch and Keras are ok

I have a dataset X of multiple series say 100 (size=100). I would like to use VAE to both denoise the data and reduce the dimensions to a smaller latent space Z (size Z << size X), because I will use this smaller latent set afterwards as an input for a NN regression problem. It's the same procedure used when we use PCA or factor analysis in order to obtain a noiseless condesate rappresentation (especially when variables share information) except …

I am working with VAEs. My input is x, which is a product of two variables $x_1$ and $x_2$. The objective (ELBO) of VAE in terms of x is: $E_{z\sim Q}[\log P(x|z)] - \mathcal{D}[Q(z|x)||P(z)]$. I want to compute the expected value of ELBO w.r.t $x_1$, i.e. $E_{x_1}[E_{z\sim Q}[\log P(x|z)] - \mathcal{D}[Q(z|x)||P(z)]]$. Given: I know the quantity: $E_{x_1}[\log P(x|z)]$. My questions are: Can I move the first expectation, I,e, $E_{x_1}$ inside expectation w.r.t $z$, i.e. $E_{z\sim Q}$? Given $z$ is sampled …

I'm searching for a way to compare mu and sigma values of the encoder network's output of variational autoencoders. In detail, imagine I trained my VAE on the MNIST digits dataset using the official training set. Then I choose 1 sample from number 5 and another one is from number 9. When I feed my numbers -which are chosen randomly, numbers 5 and 9- to the encoder network, the encoder outputs two vectors; mu and sigma. How should I compare …

From this post we can read that VAEs encode inputs as distributions instead of simple points ? What does it mean concretely ? If the encoder consists of the weights between the input image and the latent space (bottleneck layer), where is the "probability distribution" in all that ? Thank you

The following code is the KL divergence between a Gaussian posterior and mixture of Gaussian priors and it is part of the model described in this paper. The published code is written in torch language function KLDivergence(D, M) -- KL = 1/2( logvar2 - logvar1 + (var1 + (m1-m2)^2)/var2 - 1 ) local mean1_in = - nn.Identity() local logVar1_in = - nn.Identity() local mean2_in = - nn.Identity() -- [(MxN)xD] local logVar2_in = - nn.Identity() -- [(MxN)xD] local mean1 = mean1_in …

I am trying to create a 1D variational autoencoder to take in a 931x1 vector as input, but I have been having trouble with two things: Getting the output size of 931, since maxpooling and upsampling gives even sizes Getting the layer sizes proper This is what I have so far. I added 0 padding on both sides of my input array before training (This is why you'll see h+2 for the input, 931+2 = 933), and then cropped the …

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 single, normal distribution: $$ \mathcal{L}_{SIWAE}^T(\phi)=\mathbb{E}_{\{z_{kt}\sim q_{k,\phi}(z|x)\}_{k=1,t=1}^{K,T}}\left[\log\frac{1}{T}\sum_{t=1}^T\sum_{k=1}^K\alpha_{k,\phi}(x)\frac{p(x|z_{k,t})r(z_{kt})}{q_\phi(z_{kt}|x)}\right] $$ They also provide the following code which is supposed to be a tensorflow probability implementation: def siwae(prior, likelihood, posterior, x, T): q = posterior(x) z = q.components_dist.sample(T) z = tf.transpose (z, perm=[2, 0, 1, 3]) loss_n = tf.math.reduce_logsumexp( (−tf.math.log(T) + …

I have a VAE implementation that generates images from the latent distribution. I want to save those "images" as we have in the original dataset. For example, my VAE generates a data point, using following code: data_point = decoder.predict(sample_2).reshape(28,28,1) plt.figure(figsize=(4, 4)) plt.imshow(data_point, cmap = plt.cm.gray), plt.axis('off') plt.show() and I can see it as image (number 4 from MNIST). If I look at the value of data_point, it's something like this: array([[[4.03011961e-13], [2.21622661e-13], [1.77334818e-13], [7.62046296e-13], [2.77884297e-13], [2.07368519e-13], [8.03054997e-13], [2.32846815e-12], [3.30792956e-13], [5.10265875e-13], …

I have trained a VAE to generate a style transferred sentence, from a negative sentence to a positive sentence. The underlying concept of VAE tells us that the sampling is done randomly, to which Mean and Variance are added corresponding to the original input. However, with my trained VAE, I am observing that at test time, my VAE is generating the same output (style transferred sentence) given an input sentence, no matter how many times I test. My question is: …

I'm beginner in probability and statistics. I came across the concept of comparing two probability distributions. KL-Divergence and Bhattacharya(Hellinger) Distance are used to compare two probability distributions. But which one is better among these two?

I am working on a project with a variational autoencoder (VAE). The problem I have is that the encoder part of VAE is producing large log variances, which leads to even larger standard deviations, like $e^{100}$ or $e^{1000}$, which python seems to be interpreting as infinity. Thus when I sample from a distribution with this large variance, I get latent space vectors that are all infinities. These infinities then create NANs and errors when I try to train my network. …

Suppose a Variational Autoencoder (VAE) is trained with mnist data. To sample, one draws from normal distribution. My question is: suppose I am interested in generating only 1s and no other digits. How can do I do that? do I sample until I generate a 1 and then I keep sampling from the neighborhood of that point? or is there a more controlled way to tell the VAE what digit to generate? Thanks Edit* The ideal encoder would take any …

I followed this Keras documentation guide about Auto Encoders. At the end of the documentation there is the graph of the latent variable z: But I can not understand and how to interpret the plot, and how should the plot change as the hyperparameters of the model change?

I am training a variational autoencoder and I am getting a loss-plot as follows: Rigt after epoch 224, val-loss overtakes train-loss and sort of getting bigger but at an extremely slow pace as you can notice. I trained for 300 epochs. Any opinion about the training. I don't think it is overfitting the data. But I want to be sure and hence seeking opinion from the data science community. Thanks.

The VAE model I used here https://github.com/keras-team/keras-io/blob/master/examples/generative/vae.py. It can produce very well results for the minist and fashion minist dataset. But when I use my datasets, the results are pretty terrible. So I sincerely hope you can help me to provide me some guidance. The problems are as followings: My datasets are composed of many circles in the image, as shown: The shape of the datasets is [238, 28, 28, 1], and the range of pixel value is between 0 …

I'm training a variational autoencoder on CelebA dataset using TensorFlow.keras The problem I'm facing is that the generated images are not diverse enough and look kinda bad. (new) Example: What I think: it's bad because the reconstruction and KL loss are unbalanced. I read this question and followed its solution - read about KL annealing and tried to implement it myself but didn't work. Note: It's my first time working with autoencoders so maybe I missed something obvious. it would …

I'm having difficulty understanding when integrals are intractable in variational inference problems. In a variational autoencoder with observation $x$ and latent variable $z$ we want to maximize data likelihood $p_\theta (x) = \prod_{i=1}^N p_\theta (x_i)$ which is $p_\theta (x) = \int p_{\theta_1} (z) p_{\theta_2} (x|z) dz$ We know $p_{\theta_1} (z)$ (usually a Gaussian with mean $\mu$ and covariance matrix $\Sigma$), and we also know $p_{\theta_2} (x|z)$ which is usually a Gaussian distribution with mean $\mu_z$ and covariance matrix $\Sigma_z$ modeled …