Questions tagged [generative-models]

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

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7 views

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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7 views

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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9 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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6 views

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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11 views

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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35 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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14 views

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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10 views

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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30 views

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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2answers
16 views

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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16 views

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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8 views

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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1answer
25 views

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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59 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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18 views

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
84 views

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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87 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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1answer
46 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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16 views

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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14 views

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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22 views

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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1answer
60 views

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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13 views

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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10 views

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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21 views

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
25 views

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
140 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
48 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
44 views

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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19 views

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
20 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
51 views

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
49 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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0answers
98 views

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
40 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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0answers
19 views

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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1answer
72 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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2answers
270 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 ...
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1answer
130 views

Implementation of GANs [closed]

I'm working on a particle physics dataset and want to know what libraries I would need to implement GANs and other generative algorithms like in Python.
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2answers
1k views

Latent loss in variational autoencoder drowns generative loss

I'm trying to run a variational auto-encoder on the CIFAR-10 dataset, for which I've put together a simple network in TensorFlow with 4 layers in the encoder and decoder each, an encoded vector size ...
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22 views

What would happen if you trained an ML to detect artificially vs real generated voice data? [closed]

Voice data has been collected by big institituions for a really long time. Alexa and other technologies have since used them to make really human-sounding voices. What would happen if you had an ML ...
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40 views

Random Training set for GAN's [closed]

I have studies the gans in depth and some of its type like cycle, pix2pix, cgans. Now I want to generate random images from random distribution from generator. So I am creating a dataset with no ...
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1answer
745 views

GAN for inpainting an image

Is it possible to train a GAN model to inpaint an image taken from a specific setting(e.g. an office, woods, beach ...) after we have cropped out people of it? For example, I used this repo's ...
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1answer
55 views

What exactly does generator produce in DCGANs?

Does it generate the set of the same image classes in the same order on each iteration? If yes, what's the usufullness of that ?
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24 views

GAN generator output minibatch classes order

Let's take MNIST as example. I'm interested which classes(digits) images and in which order there are in generator output. I think it always generates [0, 1, 2, 3, 4, 5, 6, 7, 8 ,9] images is this ...
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54 views

Vector Arithmetic using WGAN-GP (Wasserstein GANs with Gradient Penalty)

Vector arithmetic in the latent space has been demonstrated to produce meaningful output image samples from a trained DC-GAN in the paper by Chintala et al. In fact, the vector arithmetic they ...
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1answer
1k views

How can both generator and discriminator losses decrease?

In this paper there is a plot of how the loss of gans looks through epochs. Figure 2: These are of course averaged losses. How can both the discriminator loss and generator loss decrease? ...
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1answer
72 views

Common Techniques to Generate from a Regression Neural Network Model

I am used to train neural networks that are designed for generation, such as GANs or VAEs. I am wondering what are the common techniques to generate data that would minimize the target/energy learned ...
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1answer
186 views

Can a GAN-like architecture be used for maximizing the value of a regression predictor?

I can't seem to convince myself why a GAN model similar to regGAN couldn't be modified to maximize a regression predictor (see the image below). By changing the loss function to the difference ...
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1answer
193 views

Real world example of Generative model & Discriminative model

After going over the math/stats behind the Generative model & Discriminative model, I still have no intuition about it. Does any one have a good real-world example or use case for Generative ...