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).
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questions
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1answer
30 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 ...
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11 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 ...
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0answers
16 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 ...
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1answer
22 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 ...
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1answer
40 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 ...
0
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1answer
47 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 ...
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1answer
25 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 ...
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0answers
13 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, ...
2
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1answer
43 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 ...
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19 views
GAN - relation of input size and hidden layer size
I'm adapting a GAN described here used for generating binary output. It's trained on binarized MNIST data, with a size of 28x28 so 784 values. I want to adapt it to train on and generate 1D vectors ...
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0answers
19 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 ...
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0answers
40 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 ...
0
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1answer
64 views
Decision boundary figure in Least square GAN paper
I currently reading Least Square GAN paper. But, I cannot interpret the one of the its figures. .
Explanation of the figure goes like this:
Figure 1: Illustration of different behaviors of two loss ...
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0answers
67 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'...
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14 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
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1answer
67 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 $...
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0answers
11 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
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2answers
150 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)] \\
& =...
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1answer
163 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 \...
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1answer
62 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 ...
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0answers
66 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
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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 ...
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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
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0answers
103 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 ...
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0answers
58 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 ...
3
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1answer
56 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 ...
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2answers
112 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 ...
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1answer
96 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. ...
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1answer
62 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 ...
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0answers
33 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 ...
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1answer
51 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, ...
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2answers
63 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 ...
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0answers
35 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 ...
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0answers
22 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
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1answer
36 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 ...
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0answers
17 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
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0answers
64 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 $X$...
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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
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0answers
46 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
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1answer
51 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
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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.
...
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0answers
28 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 ...
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2answers
81 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 ...
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0answers
60 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
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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
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0answers
17 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
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1answer
428 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
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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
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1answer
301 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 ...
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0answers
239 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 ...
