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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Wasserstein GAN with non-negative weights in the critic

I want to train a WGAN where the convolution layers in the critic are only allowed to have non-negative weights (for a technical reason). The biases, nonetheless, can take both +/- values. There is no ...
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87 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 ...
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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 ...
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29 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 ...
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43 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_{...
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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 ...
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How to replay an event-detection model?

Event recognition [1] is a technique to identify in a video stream a semantic action, for example “start walking”. The pixels on the screen are doing something, and the activity parser recognizes the ...
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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 ...
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59 views

Other deep learning image generation techniques besides GANs?

Can you please point me to some resources about image genereation besides GANs? Are there any other techniques throughout history? How did idea of image generation evolved and how it started? I tried ...
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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. ...
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Which libraries can be used for image caption generation?

Which libraries can be used for image caption generation?
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How important is it that the generator of a generative adversarial network doesn't take in information about input classes?

I'm building a generative adversarial network that generates images based on an input image. From the literature I've read on GANs, it seems that the generator takes in a random variable and uses it ...
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131 views

Understanding notation of Goodfellow's GAN objective function

What is the meaning of $V(D,G)$? How do we get these expectation parts? I was trying to understand it following this article: Understanding Generative Adversarial Networks (D.Seita), but, after many ...
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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 ...
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How to get good results with GAN and some thousands of images?

I'm trying to generate images at minimum of size 128 x 128 with a Generative Adversarial Network. I already tried a SAGAN pytorch implementation, but I'm not very happy with results. The images look ...
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Do we also need to model a probability distribution for the decoder of a VAE?

I'm working on understanding VAEs, mostly through video lectures of Stanford cs231n, in particular lecture 13 tackles on this topic and I think I have a good theoretical grasp. However, when looking ...
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20 views

What is the goal of the model and is the training data relevant to that?

The model that we develop in artificial intelligence.What is its purpose,and what training data is relevant to it.
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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 ...
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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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393 views

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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1answer
46 views

Is there a mathematical 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 ...
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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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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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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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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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293 views

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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114 views

Are deep learning models suitable for training with sparse data?

I am 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 ...
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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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201 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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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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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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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
90 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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560 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 ...