Questions tagged [generative-model]
Machine-learning generative models, such as the neural network architectures Restricted Boltzmann Machine, Variational Autoencoder, Generative Adversarial Network.
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Smielapp design feasible now?
Consider a SmielApp1, pronounced smile-app. It's a proposed app for Android, iOS, LINUX, and other phone, tablet, laptop, and desktop environments.2 The system requirements are a microphone, a speaker,...
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2answers
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Confusing on GAN loss function
I was trying to understand the loss function of GANs, while I found a little mis-match between different papers.
This is the screen-shot from the original paper of Goodfellow at https://arxiv.org/...
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Why is the last layer of a DBN or DBM used for classification task?
I understand why deep generative models like DBN ( deep belief nets ) or DBM ( deep boltzmann machines ) are able to capture underlying structures in data and use it for various tasks ( ...
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Giving input of condition to c-GAN
When I went through implementations of C-GAN, I found that many people concatenate the hidden layer during generator part.
So, they pass z to first layer, pass c separately to different layer and ...
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1answer
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Is there a mathemtical example for Conditional Random Fields?
I am learning about probabilistic graphical models and I was wondering if there is an example explaining the math behind Conditional random Fields. Looking solely on the formula I have no Idea what we ...
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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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2answers
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Why are Variational autoencoder's output is blurred while GANs output is crisp and has sharp edges?
I observed in several papers that the Variational autoencoder's output is blurred while GANs output is crisp and has sharp edges.
Can someone please give some intuition why that is the case? I did ...
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1answer
35 views
Class Restriction in Generative Adversarial Networks
this is my first post here.
Our problem setting:
We have to do a binary classification of data given a training-dataset D, where the majority of items belongs to class A and some items belong to ...
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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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Adding voices to voice synthesis corpuses
If one uses one of the open source implementations of the WaveNet generative speech synthesis design, such as https://r9y9.github.io/wavenet_vocoder/, and trains using something like the CMU's arctic ...
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1answer
60 views
How can AI be used to more reliably analyze and plan around the tie between climate and emissions?
Note to the Duplicate Police
This question is not a duplicate of the Q&A thread referenced in the close request. The only text even remotely related in that other thread is the brief mention of ...
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Why do we need Upsampling and Downsampling in Progressive Growing of Gans
I was working recently on Progressive Growing of GANs (aka PGGANs). I have implemented the whole architecture, but the problem that was ticking my mind is that in simple GANs, like DCGAN, PIX2PIX, we ...
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Deep learning with sparse input
I training a generative adversarial network (GAN) to generate images given edge histogram descriptor (EHD) features of the image. The EHD features are themselves sparse (meaning they contain a lot of ...
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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 ...
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1answer
127 views
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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What model is more efficient when modelling small sequences with small discrete inputs?
I Have a data where each example is having ten to twenty-time steps with small discrete input features about 50, my goal is to model P(X) as just one latent vector, i.e a hidden factor that could ...
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64 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 ...
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191 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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1answer
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Looking to build, compile, and/or find dataset for serial-parallelized code examples
I'm looking to perform two tasks:
Train a classifier to classify code as serial or parallel
Train a generative algorithm to generate parallel code from serial
For the first task a simple scraper ...
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2answers
65 views
Neural network to get input attributes using only the output value
I have an idea about how to use neural networks but I'm not sure if it is possible or not.
In supervised learning we have a set of attributes labeled with an output value. I can use these set to ...
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1answer
304 views
How to evaluate the goodness of images generated by GANs?
As we all know, there has been tons of GAN variants featuring different aspects of the image generation task such as stability, resolution or the ability to manipulate images. However, it is still ...
