Questions tagged [generative-models]

For questions about models designed for generating new data (or generating samples from a probability distribution).

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Are Denoising Variational Autoencoders deterministic?

I have a pretty good understanding of regular autoencoders and, to a certain extent, of variational autoencoders, where the latent representation is forced to follow specific probabilistic ...
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Discriminator of a Conditional GAN with continuous labels

OK, let's say we have well-labeled images with non-discrete labels such as brightness or size or something and we want to generate images based on it. If it were done with a discrete label it could ...
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How does joint probability generate new data in generative model

Learning about different Machine Learning concepts, I came across Generative and Discriminative model. To infer from what I have studied, generative model is based on P(x,y)(Joint probability ...
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33 views

Time Series Generation - Multi Dimensional Time Series Data

Disclaimer: Mathematicians please don't be mad at me for the use of some of the terminologies in this post. I am an Engineer. :-) Background: So I am currently working on a problem where I have to ...
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Help regarding generative modeling

I have data in form of n dimensional points. I have to train generative model where i pick a class through multinoulli distribution. Then each dimension of actual point is generated by some ...
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Learning discrete probability distribution that is parametrized by a set of real-valued parameters

Assume I have a discrete probability distribution defined over binary variables. This probability distribution is parametrized by a set of real-valued parameters, which all are contained in a segment, ...
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How to solve a supervised learning problem with a generative model?

Is there a framework to do supervised task in generative model fashion? i.e. modelling p(x,y) rather than p(y|x) as in discriminative models. When I look at generative models, they all revolve ...
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Why are discriminative models denoted as P(Y|X?

In general, we can speak we know deterministic and probabilistic models. Discriminative ones are deterministic, while generative one is probabilistic. But I have been reading about the difference ...
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1answer
27 views

What is a latent space vector?

I do not understand this about GANs. Apparently the Generator is supposed to receive a latent space vector as its input. Yet I couldn't find an example of how I can implement it in Pytorch. This is a ...
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Image Quality Assessment Algorithms

I am working on super resolution projects using Generative Adversarial Networks. I need to validate the generated image whether they are close to original one. I already worked with SSIM and PSNR. is ...
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14 views

Why does the generator produce output in a different scale than the training sample?

I am currently trying to train a GAN (based on proGAN) to produce images in a Vaporwave-style which is quite distinct. The results so far have been underwhelming, which I suspect might be due to a ...
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Rescaling simulations by machine learning

I am working in the field of astrophysics. A problem that I consistently find in my projects is that I am always limited by the size of the sky simulations available for a specific observable that I ...
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Is it faster and better to train a GAN on just one digit as opposed to the whole mnist dataset?

When going through an introductory GAN tutorial to generate mnist like handwritten digits I wondered whether the systematic variance in the training data due to the different digits makes the model ...
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How Cycle GAN translates between very different objects?

I'm trying to understand how the popular CycleGAN responds if the objects to be translated between are very different (horse and map or house and apples). All of the examples appear to be translating ...
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63 views

WGAN-GP slow critic training time

I am implementing WGAN-GP using Tensorflow 2.0, but each training iteration of the critic is very slow (about 4 secs on my CPU, and somehow 9 secs on Colab GPU). Is WGAN-GP usually this slow or ...
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How to grasp the full entropy of the distribution we want to model in GAN

In pix2pix GAN paper( https://arxiv.org/abs/1611.07004), authors found that the noise vector and the dropout are not efficient in grasping the full entropy of the data distribution we want to model. ...
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InfoGAN Learning Latent Categorical Code

While reading the InfoGAN paper and implement it taking help from a previous implementation, I'm having some difficulty understanding how it learns the discrete categorical code. The implementation ...
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1answer
147 views

Fully Convolutional Variational Autoencoder

I would like to make a neural network which uses black and white images as input and outputs a colored version of it. The important thing in that process is that the size of the images must stay the ...
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What are the similarities and differences between Imputation, Generative Models and Bootstrapping?

What are the similarities and differences between the following methods: Data Imputation, Generative Models, Bootstrapping.
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Why logarithm in the loss function of GAN

In the official paper of GAN by Ian Goodfellow, while maximizing the probabilities of generator and discriminator, why we take logarithm of the output of Discriminator.
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Why Generator error increases after some epoch

I'm training Realtivistic GAN on dog images. I don't understand why my generator loss increased after 100 epoch. This is the generator loss graph.
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Why does discriminiator accuracy falls to 0%, and is there a fix around this?

I am training a Vanilla-GAN(or original GAN 2016) on a pokemon dataset https://www.kaggle.com/kvpratama/pokemon-images-dataset, for few epochs the discriminator has 100% accuracy over the real ...
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How can one be assured that generative models are not memorizing dataset, and that they will generate an unique image outside of dataset?

If all GAN can do is capture the probability distribution of the dataset, then shouldn't they be similar to handing out images from the dataset? How can we verify that the images that they generate ...
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Post Training Sampling from a unit Gaussian?

During the training of a VAE, we sample Z from N(mu, sigma) ( = Q(Z|X) ) and then feed it to the decoder for reconstruction. Why do we, then, feed Z ~ N(0,1) to the decoder after the training is ...
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What does this symbol means, what operator is it?

I am confused about the $E_{x\sim P_{data}(x)}$, what does $E$ means here. I cannot find an appropriate answer on the internet, and hence I am trying data science stack exchange. Please help.
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98 views

Are mainstream pre-trained models useful as discriminators?

In the context of GANs I see many papers designing new discriminator networks. I'm curious about the usefulness of designing discriminators as modified versions of mainstream models like Inception, ...
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Problem regarding designing the generator function

I am implementing the paper Perceptual GAN for small object detection. The design is described by the picture given below. I have used Transfer Learning concept and used a pretrained model inside the ...
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2answers
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How discriminator knows if the image is real or fake at the initial phase?

If the discriminator and generator in GAN learn together, How the discriminator knows whether the image is real or fake in the initial phase of training? Does the discriminator need to get trained ...
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188 views

Comparsion between DCGAN and WGAN

What is the main architectural difference between DCGAN and WGAN? For which problems each models can be more useful than the other one?
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228 views

what is the main difference between GAN and autoencoder?

what is the main difference between GAN and other older generative models? what were the characteristics of GAN that made it more successful than other generative models?
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DC GAN to W GAN conversion

I have implemented DC GAN network and I know that changing the loss function , we get a W-GAN network, but I wonder how to code the wasserstein loss function and integrate it with my code below: Here ...
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issues related to implementation of the paper 'Perceptual GAN for small object detection'

I am trying to implement the perceptual gan network using keras as backend. I am new in the field of GAN, can anyone provide me the model for perceptual GAN in keras backend
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Issues related to the code for ROI pooling from the feature map

I am trying to do ROI pooling on the feature map obtained from the VGG layers but I don't know how to code this layers. Can anybody help me out? Here is my VGG layers: ...
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Implementation of the paper 'Perceptual Generative Adversarial Nets for small object detection'

I studied the research paper on Perceptual Generative Adversarial Nets for small object detection. There they have detailed the structure of Generator network as given in the picture below: I am new ...
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Stained glass extension GAN

I am looking for image extension GAN with support of pattern drawing/extending to a desired image size. And photo as an argument to generate corresponding result. Trained on: Areay of photos of ...
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Application of Varitiational Auto Encoders in improving the generalizability of classifiers when there is limited amount of data?

Is there any prior work on the above topic? Currently, I am working on Domain Adversarial Neural Networks and merging it with VAE to improve generalizability and transfer learning. I could not find ...
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When is bias set to False?

I have been working with DC-GAN to generate pairs of images based on WGAN paper. For which I referred fastai notebook on WGAN where the bias in the network has been set to False. Jeremy Howard in his ...
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1answer
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Generative network understanding

I was going through GAN's notebook by fchallot on Generative Adversarial Networks where, in the Generator Network, he creates a Dense layer with $16*16 * 128$ (...
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1answer
210 views

Where can I find out about the “Helvetica scenario”?

From the paper introducing GANs: It makes sense that collapsing too many $\vec{z}$-values to a single $\vec{x}$-value will cause problems. However, I was a bit confused as to how training $G$ for a ...
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1answer
63 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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1answer
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What is the interpretation of the expectation notation in the GAN formulation?

I'm confused about the expectation notation in the context of GAN loss functions. The GAN loss for the discriminator is binary cross-entropy. ie: is this real or not. real = $D(x)$ (ie: give ...
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Any heuristic for minimal DCGAN latent space dimension?

I am highly interested in approaching minimal latent space dimension (as many other may be) for DCGANs or autoencoders. In this example of DCGAN on the MNIST dataset, the person uses a 100-...
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1answer
22 views

Make embedding more Gaussian-like

I am trying to train a neural network to find a mapping(embedding) to a lower dimensional space. I would like for my dataset, once mapped to the lower dimensional space, to appear gaussian-like ...
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2answers
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What NN architecture to predict fantasy character names based on description?

I would like to build a neural network to predict a fantasy character name given a description. Like 'Scar-faced long haired elf warrior' -> 'Glorfindel' I have a dataset of about 12,000 fantasy ...
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1answer
51 views

Evaluating performance of Generative Adverserial Network?

What is the best way to evaluate performance of Generative Adverserial Network (GAN)? Perhaps measuring the distance between two distributions or maybe something else?
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What is an intuitive explanation for the Importance Weighted Autoencoder?

I have been reading a paper by Burda et al. on Importance Weighted Autoencoders(IWAE) but I can't quite grasp what they mean by sampling the terms h1...hk. Do they mean you have separate models from ...
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1answer
41 views

EGAN Paper With Confusing Notation

I am reading a research paper that tries to fix some issues with GAN, but for one of the equations, the paper does not fully explain where it comes from and why it works. Although the overall ...
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How to understand log-likelihood for generative image model?

I'm reading a paper on generative image modelling. In the paper, the authors compare various approaches by listing their "negative log-likelihoods" (see screenshot). What does this metric translate to ...
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78 views

How can generative models be used in machine learning classification applications?

My understanding of generative models is that they generate data to match certain statistical properties. Intuitively, I find it hard how generative models can be used for classification purposes. On ...
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308 views

Can Generative Adversarial Network be run on any embedded / edge device?

I am using DCGAN ( Deep Convolution GAN ) to generate images. However, I want to run it on embedded devices, such as ...