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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Can some one help me understand this paragraph from Nvidia's progressive gan paper?

Furthermore, we observe that mode collapses traditionally plaguing GANs tend to happen very quickly, over the course of a dozen minibatches. Commonly they start when the discriminator overshoots, ...
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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 ...
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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 ...
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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 ...
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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 ...
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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 ...
3
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0answers
36 views

How do GANs create an image with specific characteristics?

I've seen GANs that do things like convert an image to a painting or this GAN here https://make.girls.moe/#/ that takes in a set of characteristics and generates a waifu with those characteristics. ...
3
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0answers
28 views

How do we ensure that training GANs will fall in the desirable Nash equilibrium?

One Nash equilibrium of every GANs model has is when the generator creates perfect samples indistinguishable from the training data and the discriminator just output 1 with probability 1/2. And I ...
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 ...
2
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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 ...
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
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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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. ...
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, ...
2
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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 ...
2
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0answers
44 views

Can GANs be used to generate matching pairs to inputs?

I have some limited experience with MLPs and CNNs. I am working on a project where I've used a CNN to classify "images" into two classes, 0 and 1. I say "images" as they are not actually images in the ...
2
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0answers
135 views

How do PGMs factor in to modern ML?

I just finished the three-part series of Probabilistic Graphical Models courses from Stanford over on Coursera. I got in to them because I realized there is a certain class of problem for which the ...
2
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0answers
55 views

What kind of distributions can be used to model discrete latent variables?

If we take the vanilla variational auto-encoder (VAE), we $p(z)$ is a Gaussian distribution with zero mean and unit variance and we approximate $p(z|x) \approx q(z|x)$ to be a Gaussian distribution as ...
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0answers
85 views

How do GAN's generator actually work?

I have implemented DCGAN's myself and have been studying GAN's for over a month now. Now I am implementing the pggans but I encountered a sentence When we measure the distance between the ...
2
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0answers
112 views

Performance Evaluation Metrics used in Training, Validation and Testing

Which specific performance evaluation metrics are used in training, validation and testing and why? I am thinking error metrics (RMSE, MAE, MSE) are used in validation, and testing should use a wide ...
2
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0answers
373 views

Coding CGAN paper model in Keras

I was coding a CGAN model using Keras along with the paper (https://arxiv.org/pdf/1411.1784.pdf) and I wanted to try and match the models to exactly what the paper says. I knew the models presented in ...
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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
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 ...
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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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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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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 ...
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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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0answers
33 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 ...
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0answers
217 views

Stack for Automatic 3D Mesh Generation

Gist: Should I use LISP for a part of the following project. What are the other options. Me and a friend are planning to create a 3D Modelling Agent where a designer can :- Specify constrains on how ...
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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 ...
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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 ...
0
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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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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, ...
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0answers
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
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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0answers
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 ...
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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, ...
0
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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 ...
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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 ...
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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 ...