Questions tagged [generative-model]
For questions related to the concept of generative machine learning models, such as the Restricted Boltzmann Machine (RBM), the Variational Autoencoder (VAE), and the Generative Adversarial Network (GAN).
105
questions
4
votes
1
answer
138
views
Is plain autoencoder a generative model?
I am wondering how a plain auto encoder is a generative model though its version might be but how can a plain auto encoder can be generative. I know that Vaes which is a version of the autoencoder is ...
- 61
0
votes
0
answers
12
views
What is the exact role of model $p_\theta$ in diffusion models for the reverse process?
I'm reading this interesting blog post explaining diffusion probabilistic models and trying to understand the following.
In order to compute the reverse process, we need to consider the posterior ...
- 197
1
vote
2
answers
75
views
How to generate new data given a trained VAE - sample from the learned latent space or from multivariate Gaussian?
To generate synthetic dataset using a trained VAE, there is confusion between two approaches:
Use learned latent space: z = mu + (eps * log_var) to generate (...
- 183
1
vote
1
answer
70
views
Why don't we also need to approximate $p(x \mid z)$ in the VAE?
In the VAE, we approximate the probability distribution $p(z \mid x)$, where $z$ is the latent vector and $x$ is our data. The reason is that $p(z \mid x)$ becomes impossible to calculate for ...
- 61
1
vote
1
answer
36
views
Which formula of p(x, y) to use?
The probability distribution $p(x, y)$ can be calculated in two ways :
$p(x, y) = p(y \mid x) p(x)$
$p(x, y) = p(x \mid y) p(y)$
But according to the book Deep Generative Modeling (page number 3 ...
- 61
2
votes
1
answer
38
views
Why do we use the same parameters for the joint, marginal and conditional distributions in VAEs?
I've noticed in several resources on variational autoencoders (for example the wikipedia article), we use the same parameters theta for the prior, likelihood, posterior, etc distributions. For example ...
- 21
1
vote
1
answer
90
views
What are the steps to derive the original GAN loss function from the generalized version?
I am trying to understand how the loss function from the original GAN paper
$$\min_{G} \max_{D} V(D, G)=\mathbb{E}_{\boldsymbol{x} \sim p_{\text {data }}(\boldsymbol{x})}[\log D(\boldsymbol{x})]+\...
- 13
0
votes
0
answers
72
views
How exactly is modulation and demodulation layer in StyleGAN2 implemented?
So from the paper Analyzing and Improving the Image Quality of StyleGAN
We know that the naive way to implement the stylegan2 Conv2DMod is to compute the Style vector which has the dimension of ...
- 101
0
votes
0
answers
40
views
Interpretation of the loss function of Wasserstein GAN: the lower the better?
After following this interesting collection of tutorials for GANs
https://www.youtube.com/playlist?list=PLhhyoLH6IjfwIp8bZnzX8QR30TRcHO8Va
I've been playing around experimenting Wasserstein GAN with ...
- 197
2
votes
1
answer
41
views
Is there a way to inject linear constrains during GAN training?
Given that I'm training a generative model, (say a generative adversarial network), and I know that my (real) inputs (let's say vectors $\textbf{x} \in \mathbb{R}^n$) satisfy linear constraints of the ...
- 197
0
votes
0
answers
14
views
Incorporate specific constraints while training a (Conditional) variational autoencoder
I'm wondering how could I incorporate specific constraints during the training phase of a deep learning model.
In particular, I work for a materials-science related project where I feed to my models ...
- 197
0
votes
0
answers
13
views
Cognitive Sciences for No-Reference Perceptual Quality Assessment
I was reading on the theory of Generative Adversarial Networks, when I came upon the following article:
How to Train your Generative Models by Ferenc Huszár.
The following part left me with many ...
0
votes
0
answers
11
views
Can Dynamical Variational Auto-encoders be trained on and used to generate static 2D images?
Is it possible to train dynamical variational autoencoders, such as Kalman Variational Autoencoders (KVAE), Recurrent Variational Autoencoders (RVAE), or Disentangled Sequential Autoencoders (DSAE) on ...
1
vote
0
answers
32
views
Generative systems based on Schmidhuber's compression framework
In Driven by Compression Progress:
A Simple Principle Explains Essential Aspects of
Subjective Beauty, Novelty, Surprise,
Interestingness, Attention, Curiosity, Creativity,
Art, Science, Music, Jokes ...
- 111
0
votes
0
answers
14
views
Could current AI models reimagine Star Trek Enterprise to depict space & celestial bodies in the grandiose, astounding style of Nolan's Interstellar?
Post-Interstellar, my aptitude shifted in terms of sci-fi. I no longer can enjoy excessively speculative, unfounded predominantly fantasy subordinately scientific story telling, and imagery. I used to ...
- 101
0
votes
0
answers
34
views
What is the best way to generate handwritten text documents?
I am new to generative models. I was wondering if it would be better to generate an image of a handwritten text document as a whole (which I don't know how exactly is done), or first generate ...
- 235
0
votes
0
answers
74
views
Why batch normalization before upsampling is giving worse results?
I am training a model to generate images.
The model contains 5+5 layers:
...
- 3,099
1
vote
0
answers
68
views
How to estimate conditional density using neural network?
Conditional Variational Autoencoders (CVAE) and Mixture Density Networks (MDN) are supposed to address this issue. However, these models provide the distribution parameters, e.g., mean and standard ...
- 11
1
vote
1
answer
217
views
How does the VAE learn a joint distribution?
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}$...
- 203
3
votes
1
answer
82
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 ...
- 203
3
votes
1
answer
127
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 ...
- 33
0
votes
0
answers
132
views
Discrepencies between the TimeGan paper and the code?
I recently read the paper Time-Series Generative Neural Networks and found the results that they reported quite promising (https://proceedings.neurips.cc/paper/2019/file/...
- 41
2
votes
1
answer
62
views
What is the fundamental difference between the synthesis task and sampling task?
Among the list of tasks in machine learning, synthesis and sampling is one of the key task. Consider the following explanation regarding synthesis and sampling task from Chapter 5: Machine Learning ...
- 3,099
0
votes
0
answers
13
views
Are ranking models considered discriminative?
I'm developing a model that ranks entries based on cosine similarity to a query. Since it doesn't actually define a boundary between x and y I initially believed that such ranking models are not ...
- 1
0
votes
0
answers
34
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 ...
- 876
0
votes
1
answer
40
views
Is the range of inception score flexible or bounded based on number of classes?
Inception score is used to evaluate the generative models. It is a score given based on quality and diversity of images generated.
I have doubt about the range of inception score because of the reason ...
- 3,099
1
vote
1
answer
66
views
Is image generation not existent before generative adversarial networks?
Although the GAN is widely used due to its capability, there were generative models before the GAN which are based on probabilistic graphical models such as Bayesian networks, Markov networks, etc.
It ...
- 3,099
0
votes
1
answer
87
views
Is it possible to use deep learning to generate a 2D image from a few numerical values?
Is it possible to train a DL model that will generate a full resolution 2D image based on few numbers describing this image and what type of model or architecture would that be?
What I want to ...
- 1,039
2
votes
1
answer
84
views
Book(s) on generative models
Generative models in artificial intelligence span from simple models like Naive Bayes to the advanced deep generative models like current day GANs. This question is not about coding and involves only ...
- 3,099
0
votes
0
answers
16
views
What approaches are there to generate complex structures like syntactic trees?
What approaches are there to generate and evaluate complex structures like, let's say, syntactically correct code? I know the approach of Genetic Programming (GP) as a type of Evolutionary Algorithm, ...
- 101
2
votes
1
answer
61
views
What is the state of the art in melody generation?
Generative Adversarial Networks can generate realistic photos of people, such as thispersondoesnotexist.com. I wonder whether one can train an artificial intelligence on a batch of plain solo melodies ...
- 121
1
vote
0
answers
26
views
Necessity of likelihood in training energy-based models
Lately, I've been getting into energy-based models (EBMs) through some of Yann LeCun's recent talks, where he advocates the use of non-normalized models because it allows for more flexibility in the ...
- 11
5
votes
0
answers
45
views
Are generative models actually used in practice for industrial drug design?
I just finished reading this paper MoFlow: An Invertible Flow Model for Generating Molecular Graphs.
The paper, which is about generating molecular graphs with certain chemical properties improved the ...
- 51
0
votes
1
answer
86
views
Decision boundary figure in Least square GAN paper
I currently reading Least Square GAN paper. But, I cannot interpret one of its figures. .
Explanation of the figure goes like this:
Figure 1: Illustration of different behaviors of two loss functions....
- 174
2
votes
1
answer
74
views
Would it be possible to determine the dataset a neural network was trained on?
Let's say we have a neural network that was trained with a dataset $D$ to solve some task. Would it be possible to "reverse-engineer" this neural network and get a vague idea of the dataset $...
- 45
3
votes
2
answers
206
views
Where is the mistake in my derivation of the GAN loss function?
I was pondering on the loss function of GAN, and the following thing turned out
\begin{aligned}
L(D, G)
& = \mathbb{E}_{x \sim p_{r}(x)} [\log D(x)] + \mathbb{E}_{x \sim p_g(x)} [\log(1 - D(x)] \...
- 174
1
vote
1
answer
305
views
Optimum Discriminator for label smoothed GAN
I was reading the paper called Improved Techniques for Training GANs. And, in the one-sided label smoothing part, they said that optimum discriminator with label smoothing is
$$ D^*(x)=\frac{\alpha \...
- 174
0
votes
1
answer
83
views
Why is it easier to construct adversarial examples relative to training neural networks?
I was having looking at this lecture by Ian Goodfellow and my doubt is around 18:00 timestamp where he explains generation of adversarial examples using FGSM.
He mentions that the there is a linear ...
2
votes
0
answers
106
views
Generating fake faces containing specific features with GANs
I'm trying to understand how DeepFakes are generated and so far I understood that they're mostly generated through the usage of GANs and autoencoders.
The autoencoders part is understandable, but what ...
- 21
1
vote
0
answers
71
views
How does NN follows law of energy conservation?
Communication requires energy, and using energy requires communication. According to Shannon, the entropy value of a piece of information provides an absolute limit on the shortest possible average ...
- 67
3
votes
1
answer
98
views
How to define loss function for Discriminator in GANs?
To train the discriminator network in GANs we set the label for the true samples as $1$ and $0$ for fake ones. Then we use binary cross-entropy loss for training.
Since we set the label $1$ for true ...
- 665
0
votes
2
answers
271
views
What is the right way to train a generator in a GAN?
I am not fully understanding how to train a GAN's generator. I have a few questions below, but let me first describe what I am doing.
I am using the MNIST dataset.
I generate a batch of random images ...
1
vote
1
answer
129
views
Is it legal to license and sell the output of a neural network that was trained on data that you don't own the license to?
Is it legal to license and sell the output of a neural network that was trained on data that you don't own the license to? For example, suppose you trained WaveNet on a collection of popular music. ...
- 194
1
vote
1
answer
73
views
What is meant by degrees of freedom of latent variables?
...Designing such a likelihood function is typically challenging; however, we observe that features like spectrogram are effective when latent variables have limited degrees of freedom. This motivates ...
- 981
1
vote
0
answers
37
views
Concrete example of how transposed convolutions are able to *add* features to an image
Say we have a simple gray scale image. If we use a filter which is just the 3x3 identity matrix (or more pointedly the identity matrix but with -1 instead of the 0 entries), it is fairly easy to see ...
- 111
1
vote
1
answer
65
views
Is GAIL applicable if the expert's trajectories are for the same task but are in a different environment?
Is the GAIL applicable if the expert's trajectories (sample data) are for the same task but are in a different environment (modified but will not be completely different)?
My gut feeling is, yes, ...
- 113
0
votes
2
answers
108
views
How to combine several chatbots into one?
I'm in the middle of a project in which I want to generate a TV series script (characters answering to each other, scene by scene) using SOTA models, and I need some guidance to simplify my ...
- 3
1
vote
0
answers
44
views
How do I sample conditionally from deep belief networks?
Deep belief networks (DBNs) are generative models, where, usually, you sample by thermalising the deepest layer (as it's a restricted Boltzmann machine), and then forward propagating a sample towards ...
- 11
2
votes
0
answers
27
views
Why do hypercube latent spaces perform poorer than Gaussian latent spaces in generative neural networks?
I have a quick question regarding the use of different latent spaces to represent a distribution. Why is it that a Gaussian is usually used to represent the latent space of the generative model ...
- 121
1
vote
1
answer
41
views
Why is this variable in equation 2 of the SQAIR paper a random vector of $n$ ones followed by a zero?
I've been reading the SQAIR paper lately, and the mathematics involved seems a bit complicated.
Some background, about the paper: SQAIR stands for Sequential Attend, Infer, Repeat - the paper does ...
- 981