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).
83
questions
0
votes
0answers
26 views
Are there any good tutorials on using continuous normalizing flows (with PyTorch)?
I just have a very general question. Are there any good tutorials on using continuous normalizing flows? I'd say I have a decent understanding of normalizing flows, but not their continuous variant. I'...
0
votes
0answers
12 views
Why do the inception score and the Fréchet inception distance use the inception network and not another network?
So I was researching about the evaluation of GANs and found these two metrics which seem to be the most popular. I understand that the main ideia is to apply the data to a pre-trained network in order ...
2
votes
1answer
63 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 $...
0
votes
0answers
10 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, ...
3
votes
2answers
132 views
Mathematical Analysis of the Loss function of GAN
I was pondering on loss function of GAN and 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)] \\
& =...
0
votes
1answer
44 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 \...
0
votes
1answer
56 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 ...
0
votes
0answers
45 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 ...
0
votes
0answers
21 views
Network structure of generative model for classification
I'm trying to model a generative model for classification problem, especially aiming to solve an imbalanced data problem.
However, I couldn't get intuitive understanding for generative classifier in ...
0
votes
0answers
9 views
Given a system state, generate a sequence of state changes that lead to it
These systems are discrete and their state changes are rule based. Example:
Given a chess position, generate a series of moves that will lead to it (there may be many, one, or none, but I only need ...
2
votes
0answers
102 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 ...
1
vote
0answers
54 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 ...
2
votes
0answers
34 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 ...
0
votes
2answers
86 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
1answer
83 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. ...
0
votes
0answers
23 views
Finding the energy function given update rule of a single layer non-linear neural network
Consider the network with N neurons, each of which takes a $2 \times k$ input specified by the tuple $(\vec c_t, \vec \theta_t)$ to produce output $\vec{R}_t$ through an update rule on the pairwise ...
1
vote
1answer
58 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 ...
1
vote
0answers
31 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 ...
1
vote
1answer
46 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, ...
0
votes
2answers
53 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 ...
1
vote
0answers
34 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 ...
0
votes
0answers
249 views
Conditional Variational Autoencoder - NON Image Data
First I would like to expand an issue I've been dealing with way too long: Creating a conditional Variational Autoencoder with continuous variables in non-image data ( more specifically, time series). ...
2
votes
0answers
20 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 ...
1
vote
1answer
35 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 ...
1
vote
0answers
14 views
Using DCGAN on a (very small) dataset of art
I am developing a DCGAN using the this tutorial in PyCharm. As my usage of this tutorial suggests, I am quite new to DCGANs as I've previously only had a few experiences with machine learning ...
2
votes
0answers
49 views
Do I have to downsample the input and upsample the output of the neural network when implementing the NICE algorithm?
Consider that my input is an RGB image. The size of my image is $N\times N$. I'm trying to implement NICE algorithm presented by Dinh. The bijective function $f: \mathbb{R}^d \to \mathbb{R}^d$ maps $X$...
3
votes
0answers
31 views
Is there any way of generating fixed-length sequences with RNNs?
Is there any way of generating fixed-length sequences with RNNs? I want to tell my character level RNN to generate a name of length 3, 4, 5 and so on. I haven't found anything online like this, but my ...
2
votes
0answers
45 views
Should the RL agent be trained in an environment with real-world data or with a synthetic model?
I want to train a reinforcement learning agent in an environment with parameters (for example, the wind speed, sun irradiation, etc.) that change over time. I have recorded a limited amount of data ...
2
votes
1answer
45 views
GANs: Should Generator update weights when Discriminator says false continuously
My GANs is like this:
Train an autoencoder (VAE), get the decoder part and use as Generator
Train Discriminator
After training, do the generation in these steps:
Call Generator to generate an image
...
2
votes
0answers
35 views
Suggestion on image inpainting algorithm
Currently, many algorithms are available for image inpainting. In my application, I have some special restriction on training dataset-
Let's consider the training dataset of human facial images.
...
1
vote
0answers
27 views
Which generative methods are better for generating graphs, while preserving node and edge labels?
I started to dig into the topic of graph generation and I have a question - which out of generative methods (autoregressive, variational autoencoders, GANs, any other?) are better for generating ...
2
votes
2answers
69 views
Is it feasible to use GAN for high-quality image synthesis other than human faces?
The famous Nvidia paper Progressive Growing of GANs for Improved Quality, Stability, and Variation, the GAN can generate hyperrealistic human faces. But, in the very same paper, images of other ...
3
votes
0answers
51 views
Creating an AI than can learn to give instructions
So we think a computer is dumb because it can only follow instructions. Therefor I am trying to create an AI that can give instructions.
The idea is this: Create a geometric scene (A) then make a ...
3
votes
0answers
124 views
Speaker Identification / Recognition for less size audio files
I am working on speaker identification problem using GMM (Gaussian Mixture Model). I have to just identify one user present in the given audio, so for second class noise or silent audio may use or not ...
2
votes
0answers
16 views
Improving graph decoder network
I have been using a network to generate graphs. The architecture that I have been using is the following:
In this figure, $D_1$ is the signal generator and $D_2$ is the graph topology generator, ...
4
votes
1answer
288 views
What is the input for the prior model of VQ-VAE?
I'm trying to implement the VQ-VAE model. In there, a continuous variable $x$ is encoded in an array $z$ of discrete latent variables $z_i$ that are mapped each to an embedding vector $e_i$. These ...
2
votes
1answer
57 views
Giving an AI a purpose to talk
I am trying to teach my AI to talk. The problem is I'm struggling to find a good scenario in which it needs to.
Some ideas I had were:
"Describe a geometric scene" - Then together with a parser we ...
4
votes
1answer
195 views
Does MMD-VAE solve the problem of blurred images of vanilla VAEs?
I understand that with vanilla VAEs, there are a few reasons justifying the production of blurred out images. The InfoVAE paper describes the case when the decoder is flexible enough to ignore the ...
2
votes
0answers
139 views
Are there any general tips for troubleshooting a VAE when apparently it is not learning?
I am trying to train a VAE for anomaly detection. I chose one architecture from this Github repository and I adjusted the input and output to match what I need. In my case, the input (and hence the ...
5
votes
1answer
157 views
Context-based gap-fill face posture-mapper GAN
These images are handmade, not auto-generated like they will be in production. Apologies for inaccuracies in the graph overlay.
I am trying to build an AI like that displayed in the diagram: when ...
3
votes
1answer
69 views
How exactly does adversarial training help in handling mode-collapse in generative networks?
Of my understanding mode-collapse is when there happen to be multiple classes in the dataset and the generative network converges to only one of these classes and generates images only within this ...
1
vote
1answer
79 views
Why does the discriminator minimize the cross-entropy while the generator maximize it?
In his original GAN paper Goodfellow gives a game theoretic perspective for GANs:
\begin{equation}
\underset{G}{\min}\, \underset{D}{\max}\, V\left(D,G \right) =
\mathbb{E}_{x\sim\mathit{p}_{\...
1
vote
0answers
32 views
What's a good generative model for creating valid formats of a person's name?
I'm trying to come up with a generative model that can input a name and output all valid formats of it.
For example, "Bob Dylan" could be an input and the gen model will output "Dylan, Bob", "B ...
0
votes
1answer
26 views
How to change this RNN text classification code to become text generation code?
I can do text classification with RNN, in which the last output of RNN (rnn_outputs[-1]) is used to matmul with output layer weight and plus bias. That is getting a word (class name) after the last T ...
3
votes
2answers
52 views
How can we find find the input image which maximizes the class-probability for an ANN?
Let's assume we have an ANN which takes a vector $x\in R^D$, representing an image, and classifies it over two classes. The output is a vector of probabilities $N(x)=(p(x\in C_1), p(x\in C_2))^T$ and ...
3
votes
1answer
245 views
Which approach can I use to generate text based on multiple inputs?
I have a little experience in building various models, but I've never created anything like this, so just wondering if I can be pointed in the right direction.
I want to create (in python) a model ...
2
votes
1answer
145 views
What is the purpose of the noise injection in the generator network of a GAN?
I do not understand why with enough training how the generator cannot learn all images from the training set as a mapping from the latent space - It is the absolute optimal case in training as it ...
0
votes
1answer
584 views
Query regarding the minmax loss function formulation of the training of a Generative Adversarial Network (GAN)
Just needed a clarification on the training procedure for a standard GAN.
Of my understanding the loss function to optimize is a min max (max min causing mode collapse due to focus on one class ...
0
votes
1answer
66 views
If the goal of training of a GAN is to have $P_g=P_{data}$, shouldn't this produce the exact same images?
Referring to the blog, Image Completion with Deep Learning in TensorFlow, it clearly says that we would want a generator $g$ whose modeled distribution fits our dataset $data$, in other words, $P_{...
3
votes
1answer
775 views
What parameters can be tweaked to avoid a generator or discriminator loss collapsing to zero when training a DC-GAN?
Sometimes when I am training a DC-GAN on an image dataset, similar to the DC-GAN PyTorch example (https://pytorch.org/tutorials/beginner/dcgan_faces_tutorial.html), either the Generator or ...
