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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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 ...
11
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3answers
396 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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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 ...
7
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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 ...
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 ...
5
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1answer
162 views

Context-based gap-fill face posture-mapper GAN

These images are handmade, not auto-generated like they will be in production. Apologies for inaccuracies in the graph overlay. I am trying to build an AI like that displayed in the diagram: when ...
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 ...
4
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1answer
208 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 ...
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 ...
4
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1answer
306 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 ...
3
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2answers
54 views

How can we find find the input image which maximizes the class-probability for an ANN?

Let's assume we have an ANN which takes a vector $x\in R^D$, representing an image, and classifies it over two classes. The output is a vector of probabilities $N(x)=(p(x\in C_1), p(x\in C_2))^T$ and ...
3
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2answers
137 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)] \\ & =...
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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1answer
257 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 ...
3
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1answer
862 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 ...
3
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1answer
70 views

How exactly does adversarial training help in handling mode-collapse in generative networks?

Of my understanding mode-collapse is when there happen to be multiple classes in the dataset and the generative network converges to only one of these classes and generates images only within this ...
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 ...
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 ( ...
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 ...
3
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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 ...
3
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0answers
54 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 ...
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. ...
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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1answer
64 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 $...
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 ...
2
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2answers
70 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 ...
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 ...
2
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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/...
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 }}(\...
2
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1answer
45 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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1answer
152 views

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 ...
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 ...
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
102 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
34 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 ...
2
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0answers
20 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
54 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 $X$...
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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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
164 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
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 ...
2
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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 ...
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 ...
2
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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 ...
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 ...
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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.
1
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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 ...