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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3
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1answer
868 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 ...
2
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0answers
43 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 ...
3
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1answer
85 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 ...
3
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0answers
35 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. ...
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2answers
106 views

Which libraries can be used for image caption generation? [closed]

Which libraries can be used for image caption generation?
2
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2answers
111 views

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 ...
10
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1answer
1k 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 ...
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 ...
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1answer
153 views

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 ...
3
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2answers
118 views

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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1answer
23 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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0answers
123 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 ...
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3answers
1k 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/...
3
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1answer
120 views

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 ( ...
2
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1answer
127 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 ...
2
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0answers
54 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 ...
5
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2answers
5k views

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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0answers
216 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 ...
1
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1answer
26 views

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 ...
2
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1answer
84 views

Why do we use $D(x \mid y)$ and not $D(x,y)$ in conditional generative adversarial networks?

In conditional generative adversarial networks (GAN), the objective function (of a two-player minimax game) would be $$\min _{G} \max _{D} V(D, G)=\mathbb{E}_{\boldsymbol{x} \sim p_{\text {data }}(\...
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1answer
92 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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2answers
400 views

What are the best machine learning models for music composition?

What are the best machine learning models that have been used to compose music? Are there some good research papers (or books) on this topic out there? I would say, if I use a neural network, I would ...
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1answer
666 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 ...
4
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1answer
2k 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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0answers
83 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 ...
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3answers
397 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, ...
2
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0answers
111 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
371 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 ...
4
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1answer
36 views

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
108 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 ...
3
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1answer
808 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 ...
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1answer
847 views

What is the purpose of the GAN?

The Generative Adversarial Network (GAN) is composed of a generator $G$ and a discriminator $D$. How do these two components interact? What is the intuition behind the GAN, its purpose, and how it is ...
2
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2answers
104 views

What are the possible social consequences of training neural networks with artificially generated data?

Machine learning models and, in particular, neural networks are trained with data often collected from the real world, such as images of real people. Meanwhile, neural networks (such as GANs) are also ...
20
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3answers
25k views

How can we process the data from both the true distribution and the generator?

I'm struggling to understand the GAN loss function as provided in Understanding Generative Adversarial Networks (a blog post written by Daniel Seita). In the standard cross-entropy loss, we have an ...

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