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Variational AutoEncoder using a matrix as dataset with a time component or ...?

The project is to repair data/smoothing. I would like to use a matrix of shape (5,13,3922): 5 differents types 13 features, one of them is the Times series, dates in format yyyy-mm-dd that I turned ...
Adurrow's user avatar
1 vote
1 answer
15 views

Reduce mode searching behaviour of VAE

I'm applying VAEs to sections genomic data (haplotypic vcf format, so binary variables), with one model being trained on each section. They each have different layer sizes and weights to better fit ...
Whitehot's user avatar
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19 views

Why posterior in EM algorithm tractable but in VAE not?

So I've read through this post, including the Bayesian Mixture of Gaussians supplied in the link in the last comment by oW_ there, to really see why the posterior is intractable. I just don't see yet ...
Anon's user avatar
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Why Latent Space in Stable Diffusion has shape 64x64x3?

I am wondering why the dimensionality of Latent Space in Stable Diffusion is 64x64x3. Since ...
Renat Abdrakhmanov's user avatar
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48 views

Unsupervised Log Anomaly Detection

I am thinking about using the variational autoencoder model for anomaly detection . I have an Android Logs dataset. As the logs generated are a representative of time series type of data I thought ...
MLenthusiast's user avatar
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13 views

Why Prior VAE works very well on training but very suck in eval?

I'm doing a task with "Proir VAE" The Proir VAE Means it doing KL with a learned Prior encorder so that it can leverages it instead of normal distribution. And in inference time, it can ...
KEIFTH YANG's user avatar
1 vote
1 answer
44 views

Not understanding how to eval a VAE model?

As I understanding the VAE, it's a model to get the P(x) of x(final job like image generation). When i train it, It input x from dataset to get mu and var from encoder, and to get a sample z from mu ...
KEIFTH YANG's user avatar
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25 views

Can't show images outputted by a VAE with pyplot.imshow - wrong dimensions

I'm trying to show images generated by a variational autoencoder using pyplot.imshow and make_grid. I can't show them, though, with the following error: "TypeError: Invalid shape (64, 530, 42) ...
avpol's user avatar
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Data generation with correlated columns

I am working on a finance project where i have to work on a model that predicts the short term impact on the bonds market (on the Spreads yield) when a big trade happens based on it's duration, ...
AhYas02's user avatar
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13 views

VAEs - getting an equivalent sample?

I'm trying to learn/experiment with VAEs(I'm a complete beginner). If I get a probability distribution Q closely similar to the original distribution P using a VAE, would it be possible to find a ...
user45788's user avatar
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Variational Autoencoder Multi-Class Interpolation

I'm working on a variational autoencoder (VAE) with 20 different classes in my training data. I've successfully trained the VAE and can sample from the latent space to generate data points. However, I ...
charactercapital's user avatar
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Similiar reconstruction for Pytorch VAE

This is my first question here, so if I don't offer enough information for my question to be answered, please let me know. I am currently working on my Bachelor Thesis, in which I aim to integrate ...
Felix Körber's user avatar
1 vote
1 answer
312 views

How does a VQ-VAE produce new images?

I'm implementing a VQ-VAE for a LDM for biological time series data. I trained the VQ-VAE, and reconstructions works somewhat reasonable, but I have an understanding problem with how a VQ-VAE works. ...
Jackilion's user avatar
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102 views

Is it a good idea to use attention in VAEs for image generation?

There are research papers and codebases on GitHub that deal with VAEs for image generation on popular datasets like CelebA, etc. While surfing through Google Scholar I found self-attention and other ...
Sir Arthur7's user avatar
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1 answer
75 views

What neural network architecture would help me model a spectrogram?

I'm really a novice working with these technologies and I'm struggling to design a neural network that is powerful enough to model a spectrogram. For a personal project, I'm working on a spectrogram ...
BOBONA's user avatar
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estimate p(z|D) in a VAE

I have a maybe naive question, but is it easy to estimate p(z|D) for a variational autoencoder where $D = (x_1, \dots, x_n)$ is an iid training dataset ? If we write the derivation, we get : $ p(z|D) =...
Ezzine Léna's user avatar
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28 views

Binary latent representation

I've been working on a problem where I got stuck at encoding my data into a binary latent representation, most of the methods out there aren't really working for my case. I have input for the encoder ...
HAMDI ABDERRAHMENE's user avatar
4 votes
2 answers
370 views

How does variational autoencoders actually work in comparison to GAN?

I want to know about how variational autoencoders work. I am currently working in a company and we want to incorporate variational autoencoders for creating synthetic data. I have questions regarding ...
NevMthw's user avatar
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1 answer
387 views

What is the dataset during testing a Variational auto-encoder?

I am getting confused in the testing dataset of a VAE. After training the VAE, what should be the testing data-set of the VAE? I understand that during testing the VAE only has the decoder part. Hence,...
Formal_this's user avatar
1 vote
1 answer
89 views

Does minimizing kl divergence (i.e. keep approximate posterior close to prior) contradict the goal of avoiding posterior collapse?

Posterior collapse means the variational distribution collapse towards the prior: $\exists i: s.t. \forall x: q_{\phi}(z_i|x) \approx p(z_i)$. $z$ becomes independent of $x$. We would like to avoid it ...
JXuan's user avatar
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70 views

Sigmoid Activation Function (Output layer) Alternative

I have a Convolutional-VAE architecture where the target images are in the range [0, 1], their pixel values. To synthesize/reconstruct images in this scale, I am using a sigmoid activation function in ...
Arun's user avatar
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1 vote
0 answers
13 views

Why only discrete labels are used for (semi-)supervised VAEs?

I've noticed all semi-supervised VAEs assume discrete (categorical) labels to encourage disentangled representation learning in VAEs. e.g., Kingma, Durk P., et al. "Semi-supervised learning with ...
MerelyLearning's user avatar
1 vote
1 answer
2k views

KL divergence loss first decreases and then increases in VAE training

I am training a VAE on CelebA HQ (resized to 256x256). The training is going well, the reconstruction loss is decreasing and reconstructions are also meaningful. But, the problem is with KL divergence ...
RajaParikshat's user avatar
0 votes
1 answer
119 views

Generate new distribution from auto-encoder /variational autoencoder

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 ...
Sparsh Garg's user avatar
1 vote
0 answers
86 views

How should I think when I want to compare mu and sigma for different images in VAE?

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 ...
BlackCode's user avatar
1 vote
0 answers
120 views

What does it means (concretly) that a VAE encode inputs as distribution?

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 ...
RandomFellow's user avatar
1 vote
1 answer
179 views

Latent variable graph in Variational Autoencoder

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, ...
Turned Capacitor's user avatar
0 votes
1 answer
885 views

Controlling the sampling from Variational AutoEncoder (VAE)

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. ...
Schach21's user avatar
  • 103
0 votes
1 answer
365 views

Help needed in interpreting the loss, val_loss vs epoch plots for an autoencoder training?

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 ...
user62198's user avatar
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1 vote
0 answers
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Training with different datasets for the same better VAE model yields poor results

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 ...
Guo Ray's user avatar
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4 votes
1 answer
4k views

1D CNN Variational Autoencoder Conv1D Size

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 ...
Celi Manu's user avatar
  • 141
2 votes
1 answer
246 views

What are the Most Dissimilar MNIST Digits?

Using whatever definition of dissimilarity over sets that you'd like, what are the most dissimilar two digits in MNIST? I was thinking that a reasonable approach to answering the question would be to ...
JoeTheShmoe's user avatar
1 vote
0 answers
203 views

pytorch code for VAE for MINST forces mu and logvar to zero

I am new to pytorch and trying to implement a VAE for MNIST data. When I try to train my model, it appears that the model forces mu and logvar to zero (or something very close to zero) independent of ...
yorkiva's user avatar
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1 vote
0 answers
147 views

Is vanishing KL a problem in vision based VAEs as well?

I came across some work on the problem of a vanishing KL contrbution in Variational Auto Encoders Cyclical Annealing Schedule: A Simple Approach to Mitigating KL Vanishing. This work particularly is ...
Matt's user avatar
  • 125
4 votes
1 answer
683 views

Why KL Divergence instead of Cross-entropy in VAE

I understand how KL divergence provides us with a measure of how one probability distribution is different from a second, reference probability distribution. But why are they particularly used (...
Bahauddin Omar's user avatar
0 votes
1 answer
1k views

What makes the posterior intractable?

In the setting of Variational AutoEncoders, i.e. when we want to find the posterior distribution over the data generating, latent variable z, given some ...
Matt's user avatar
  • 125
3 votes
1 answer
2k views

VAE generates bad images. due to unbalanced loss functions?

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 ...
Lidor shimoni's user avatar
1 vote
1 answer
524 views

How to make custom callback in keras to generate sample image in VAE training?

I'm training a simple VAE model on 64*64 images and I would like to see the images generated after every epoch or every couple batches to see the progress. when I train the model I wait until the ...
Lidor shimoni's user avatar
9 votes
1 answer
513 views

Train a GAN on "before and after" images of dental surgeries [closed]

I want a GAN to train on "before and after" images of dental surgeries; so that it can generate "after" pictures for fresh patients. Input images are like these: https://img.webmd.com/dtmcms/live/...
Lakshay Dulani's user avatar
2 votes
2 answers
70 views

Can VAEs be used to generate multivariate data?

Most of the tutorials online seem to use VAEs to generate images and use CNNs to generate data. I am working on a game with multivariate data consisting of character position and the character ...
Alex's user avatar
  • 21
1 vote
0 answers
261 views

Variational Autoencoder: Negative log likelihood not optimized

I am using the auto encoding variational Bayes algorithm for one unsupervised object detection task. In the loss function, the reconstruction loss is calculated as the log likelihood of the original ...
Ricky's user avatar
  • 189
1 vote
0 answers
225 views

How to estimate total correlation KL[q(z)||Πjq(zj)] of VAE after training (useful for latents disentanglement evaluation)

FactorVAE and β-TCVAE both use total correlation (TC) batch estimation for their objectives. Where TC is: $$ KL\bigl( q(z)||\prod\nolimits_{j} q(z_{j})\bigr) $$ both estimates are applied to $q(z|x)$...
Peter Zagubisalo's user avatar
0 votes
2 answers
440 views

What is the meaning of "probability distribution of p(x)" of something uncountable?

I'm studying VAE and new to both of the neural network and the statistic. After some researches, I could understand the rough concept of VAE. But what makes me confused is, the meaning of probability ...
Newbee's user avatar
  • 3
4 votes
1 answer
2k views

Intractability in Variational Autoencoders

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 ...
KRL's user avatar
  • 231
6 votes
1 answer
6k views

Why maximize ELBO in the variational autoencoder?

For a variational autoencoder, we have that: $$\mathcal{L}(x,\theta,\phi) := \mathbb{E}_{z \sim q_\phi(z|x)}[\log p_{\theta}(x|z)] -KL[q_{\phi}(z|x) ||p(z)] $$ This is called the variational lower ...
A_P's user avatar
  • 163
5 votes
1 answer
8k views

ValueError: Cannot convert a partially known TensorShape to a Tensor: (?, 256)

I'm working on a sequence to sequence approach using LSTM and a VAE with an attention mechanism. ...
Kahina's user avatar
  • 634
4 votes
1 answer
7k views

What do we visualize in showing a VAE latent space?

I am trying to wrap my head around VAE's and have trouble understanding what is being visualized when people make scatter plots of the latent space. I think I understand the bottleneck concept; we go ...
ITA's user avatar
  • 143
21 votes
1 answer
25k views

What is "posterior collapse" phenomenon?

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 ...
thanatoz's user avatar
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