All Questions
Tagged with generative-models neural-networks
97 questions
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Why do VAEs work?
I am currently reading into Variational Autoencoders, and although I kind of understand the mathematical background described in the original paper (Auto-encoding Variational Bayes), I am struggling ...
1
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0
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65
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Is it possible to explain regression or classification, interpolation and generation using a single model structure?
Neural network is established as an universal approximator of all machine learning models. Further, double descent phenomenon in a neural network propagates the journey of regression to interpolation ...
- 81
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35
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How to achieve Voice Conversion Using Voice Samples of a Specific Person using any voice as input?
I'm working on a project involving voice conversion, aiming to transform a voice to sound like a specific person speaking Darija (a Moroccan Arabic dialect). I have collected a set of voice samples ...
- 11
1
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1
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57
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NeRF vs mesh for text-to-3d generation
There seem to be multiple aproaches to generating 3d objects from text prompt. What's confusing is that some of them are generating NeRFs (https://arxiv.org/pdf/2308.16512), other's are generating ...
- 196
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174
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Maximum Mean Discrepancy (MMD) implementation as a metric to measure GAN performance [closed]
I am trying to evaluate the performance of the GAN model, I have trained. I found that there exist two major choices FID (Fréchet inception distance) and MMD (Maximum Mean Discrepancy) for comparing ...
- 21
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1
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459
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What kind of Neural Networks are required for Diffusion models?
It appears that regular feed-forward and convolutions are not enough to make diffusion models work (from some personal limited testing, they do not work at all). The typical infrastructure was a U-Net ...
- 768
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1
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89
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In factor VAE, do you freeze the discriminator weights during the back propagation step for the FVAE loss?
In factor vae, Disentangling by Factorising, there are two losses that are minimized. One is the VAE loss (eq. 2 in the paper) that includes (1) reconstruction loss, (2) KL divergence and (3) Total ...
- 33
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1
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114
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VAE active units
According to Burda et al (2015) number of active units is computed as:
$ Cov_x(E_{z \sim q_\phi(z|x)}) > \delta $
for some particular delta. In the paper it is set to 0.02 empirically. But this ...
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1
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102
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Normalizing Flows Invertibility
I am currently reading up on RealNVP, which has the following transformations according Lilian Weng:
$$
\begin{aligned}
\mathbf{y}_{1:d} &= \mathbf{x}_{1:d} \\
\mathbf{y}_{d+1:D} &= \mathbf{x}...
- 19
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1
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1k
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How to generate new data with a VAE?
I have built the following function which takes as input some data and runs a VAE on them:
...
- 531
4
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1
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1k
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How to rewrite DreamBooth loss in terms of $\epsilon$-prediction?
I'm trying to make the loss used in DreamBooth paper explicit, writing it in terms of the noise, as it is commonly written in the original diffusion article [1], instead of the image reconstruction ...
- 505
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464
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If neural networks are deterministic once trained how are generative able to generate different responses to inputs?
Once training is finished and a neural network is in inference mode, its outputs will be deterministic, i.e. the weights have been fixed. How is it then that generative models are able to generate non-...
3
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2
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897
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How is the variance for a diffusion kernel derived for a diffusion model?
So I'm watching this video tutorial from CVPR this year on diffusion models, and I am confused by the variance term in the distribution on the left on the video. I understand that in the forward ...
- 31
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2
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2k
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Can the dimension of the latent space in VAEs, be larger than the dimension of the data?
I am experimenting with VAEs. There, there is a parameter that you need pass when you create the NN, which is the dimension of the latent space.
In the typical ...
- 531
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244
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What's GAN's Input-size Limitations?
I am interested in GAN for generating synthetic data. I am studying the input limitations for GAN starting from which GAN is no longer usable.
I have found many applications that use GANs for ...
- 101
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389
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Classical VAE not learning 2D gaussian mixture distribution using MSE loss
I've been exploring VAE for non-image data. I consider small to medium-sized continuous vector spaces and I want to learn the distribution of a dataset in that space.
As a warm up exercise, I tried ...
- 113
2
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1
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967
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Initialization of GAN discriminator
The question is pretty straightforward: how are GAN and WGAN discriminators typically initialized?
I couldn't find much info on this. E.x. for GANs, I imagine you would theoretically want the ...
- 143
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105
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Restricted Boltzmann Machine: W matrix visualization results after training MNIST images and Pseudo-log-likelihood
I am implementing RBM from scratch using Tensorflow and after training my RBM on the MNIST dataset for 200 epochs using Persistent CD with two steps of contrastive divergence, I learn the weights W ...
- 21
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1
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48
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Is there such a thing as intra-sample modal collapse in GANs?
Mode collapse is a known issue in generative adversarial networks (GANs) whereby the generator only learns a subset of the real data distribution. In those cases, it only outputs variations of a small ...
- 169
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220
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Synthetic data generation - GANs vs Simulator?
For synthetic data generation, does the GAN perform better than a simulator? If so, what are the limitations of the simulator?
If we consider Conditional GANs, we could generate data based on the ...
- 111
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418
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I need help understanding the meaning of the loss values of a WGAN with Gradient Penalty
I am currently working on training a Auxiliary Classifier Wasserstein GAN with Gradient Penalty. I based my implementation off of https://keras.io/examples/generative/wgan_gp/ (to which I added the ...
2
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1
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297
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Issues with GAN and VAE models
I'm reading this amazing paper on Normalizing Flows https://arxiv.org/pdf/1908.09257.pdf but one sentence kind of bothers me:
GANs and VAEs have demonstrated impressive performance results on
...
1
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1
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50
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Is the density of PixelCNN normalized?
PixelCNN++ constructs a model distribution $p(x)$ over images $x\in\mathbb{R}^{n\times n}$ as a product of conditional distributions over pixels
$$p(x)=p(x_1,...,x_{n^2})=\prod_{i=1}^{n^2} p(x_i| x_1,....
2
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2
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414
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What concept comes before VAE and after GMM?
Suppose I am designing a course on generative models and I have just finished discussing GMM. My goal is to teach VAE.
However, VAE's technicality is very high. Does there exist some model in between ...
- 734
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1
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49
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Can a Simple ANN be Generative?
If a simple ANN was trained to predict the next step in a sequence, such as a univariate time series, can it be considered a generative model?
- 21
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1
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1k
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How to find bits/dim of a gaussian output distribution?
I have images that are 64x64x3 and 64x64x1 8-bit. I transform those images down to [-1,1]. I now want to find the bits/dim for my VAE log probability. How do I find the bits/dim of the log likelihood? ...
- 65
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1
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2k
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When is my Wasserstein GAN-GP overfitting?
I have a hard time interpreting the WGAN-GP losses attached. At which epoch is D and/or G overfitting? The quality improves a lot overtime, yet the generator loss at later epochs does not appear on ...
2
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1
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480
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Can PCA generate a new random image?
I read The Batch: GANs newsletter and Goodfellow said:
My colleague Bing Xu modeled face images from the Toronto Face Database, which were only 90 pixels square and grayscale. Because the faces were ...
- 121
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148
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Probabilistic Difference between Autoencoders and Variational Autoencoders
I have recently read up about Autoencoders and Variational Autoencoders. In Variational Autoencoders, the loss is modeled based on what distribution we choose for P(x|z). So, if we choose it to be ...
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3
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2k
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In VAE, why use MSE loss between input x and decoded sample x' from latent distribution?
Variational Autoencoders (VAEs) are based on the concept of Variational Inference (VI) and use two Neural Networks similar to Vanilla Autoencoders (AEs) for function approximation. I understood the ...
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4
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2
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2k
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Variational Autoencoder (VAE) latent features
I'm new to DL and I'm working on VAE for biomedical images. I need to extract relevant features from ct scan. So I created first an autoencoder and after a VAE. My doubt is that I don't know from ...
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1
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2k
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What is the Intuition behind the GAN Discriminator loss? How does Discriminator loss works?
I have just stated learning GAN and the loss used are different for same problems in same tutorial. Could someone please tell me intutively that which loss function is doing what?
For example, in the ...
- 244
3
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2
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7k
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What is the stop criteria of generative adversarial nets?
I have used the GANs (Generative Adversarial Networks) with a binary cross-entropy loss function (in both generator and discriminator). Throughout the training step, the variation of generator loss ...
- 51
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1
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130
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Generating text using LSTM given condition vector
I know that you can use an RNN to generate text given the first few letters
...
- 163
1
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1
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2k
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Understanding reparameterization trick and training process in variational autoencoders
I am trying to understand variational autoencoders, particularly the sampling component and the reparameterization trick. I understand that instead of using a fixed determinstic latent representation ...
- 1,030
3
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1
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444
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Can someone explain CAM loss used in U-GAT-IT paper?
I have been reading a recent paper accepted at ICLR, U-GAT-IT, which seems to produce pleasing results in the image-to-image translation tasks. There are four kinds of loss used in this paper: ...
0
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1
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156
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why predict a distribution in pixelcnn++ [closed]
I know that in the original pixelcnn paper, they predicted a 255 vector for each subpixel, and argmaxed to get the value.
in the pixelcnn++ paper, if I understand it correctly, they model the pixel ...
- 273
3
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1
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209
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Why is using a mixture of logstic distributions makes sense in pixelcnn++?
I went trough the paper and code of the pixelcnn++ model.
From what I understand, they train the network in the following way for predicting the value of a single pixel:
the inputs are the pixel ...
- 273
2
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1
answer
3k
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A meaning/significance of validation loss in a Generative Adversarial Neural Network? [closed]
On most of the tutorials on GANs that I came across the only monitored quantity is training loss.
1) Are there any general conclusions that could be derived from comparing training and validation ...
- 43
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1
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557
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How do I resize output of a generative network to reconcile with the size of the real data?
I am designing a Generative Adversarial Network (GAN) trained on an image dataset. It has two components: the generator and the discriminator. The generative network outputs an artificial image. The ...
- 218
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0
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66
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One shot inference with Variational Autoencoders using proposal mean
Let's say you have an already trained Variational Autoencoder where the parameters are $\phi, \theta$ for the recognition and generative models respectively. Let's also assume you have the following ...
- 111
3
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1
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78
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Why are autoregressive models neither directed or undirected, as described in the NADE paper?
In the paper Neural Autoregressive Distribution Estimation (Uria et al., 2016), NADE (and other autoregressive models) seem to be described as neither directed or undirected models:
We’ve described ...
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116
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Generating Graphs using a Neural Network
I currently have constructed a Graph Neural Network in PyTorch with graph conv layers I have made. With this, I am able to feed in adjacency and feature matrices and successfully perform node ...
- 176
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2
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How to extract crucial features to create an image
Imagine, you have a dataset containing pictures of (example only, just to explain the task) cats and dogs. The data set is labeled, so we can train using supervised learning algorithms.
My goal is ...
8
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1
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204
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Structure of Generative Adversarial Networks (GAN) for mapping a simulation model
There is a simulation model of a system that I want to map as a neural network to test if a better execution time can be achieved with similar accuracy.
The simulation model receives real-valued ...
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4
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1
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156
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Deep generative models learning a Bayesian-network distribution
Say I have a generative model for some distribution $p$ over a small number of RVs which allows me to easily sample from said distribution. For example, let's say it's a parameterized Bayesian network ...
- 451
13
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1
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730
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How to understand Generative Adversarial Networks Discriminative distribution?
So I am currently studying Generative Adversarial Network and I read the paper by Goodfellow a few times now Generative Adversarial Nets and a few other papers in this field (DCGAN, CycleGAN, pix2pix, ...
- 235
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168
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Are Flow Based Generative Models Referring to the Invertible Transformations?
So I have been studying generative models for a while now. I know how GANs and VAEs work quite well, but I am quite confused by how Flow Based Generative Models work.
To my understanding, flow based ...
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1
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469
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Does variational auto-encoder output the variational distribution of the latent variable or the distribution of the input x?
In the simple case of mixture of gaussians(with known variance), we have 2 latent variables $\mu$ and $z$. In the vaiational auto-encoder, we assume that the model is infinite mixture of gaussians. If ...
- 1,382
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1
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91
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Learning Deep Generative Models of Graphs
I'm reading through Learning Deep Generative Models of Graphs, which is a paper that seems to me propose some sort of variational autoencoder to generate a graph.
At very high level the semantic of ...
- 213