Questions tagged [generative-models]

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

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How Reconstructing the Original Image and Cycle Consistency Constraints help the GAN in image to Image Transalation

Currently, I am reading few papers related to image to image translation using Generative Adversarial Network. What I have found is that many papers involves Reconstructing the same original image ...
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MNIST GAN generator loss increasing

I'm trying to train a simple vanilla GAN on MNIST with Tensorflow. I used this github page as a reference and in my process to try and get my GAN to work I've made my code more and more similar to ...
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Variational Autoencoders(VAE)-zero variance problem

So, i'm having a problem with training my VAE. I'm not sure if i'm dealing with a bug in code or a bug in logic/understanding of the topic. Here is an image showing latent variable variances on test ...
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During modeling binarized handwritten digit images using a BMM and EM algorithm, why must, during initialization, the value of means sum to 1?

This is my first question on StackExchange. So please pardon any mistakes that I might've made. I'm reading about Multivariate Bernoulli Mixture Models and the EM algorithm from Pattern Recognition ...
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33 views

How to make custom callback in keras to generate sample image in VAE training?

I'm training a simple VAE model on 64*64 images and I would like to see the images generated after every epoch or every couple batches to see the progress. when I train the model I wait until the ...
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41 views

Is it possible to train stylegan2 with a custom dataset using a graphics card that only has 6GB of VRAM (GeForce GTX 1660)?

I'm attempting to train stylegan2 using a custom dataset, but no matter what settings I use I see the same error: ...
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generative models visualizations

Can someone recommend me a paper or blog where they show nice visualizations of reconstructed data in a generative model, which is not trained with pictures. Visualization of reconstructed images is ...
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WGAN training tradeoffs

Currently training a WGAN with weight clipping and having reread the architecture and pitfalls mentioned in code, I am running it with 2 layers in critic and generator, no batch norm in generator and ...
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Order of importance of hyperparameters

I'm trying to make a generative model and for a given input shape there is a fixed corresponding output shape. Because of the way convolutional and transposed convolutional layers determine output ...
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Using DCGAN on a (very small) dataset of art

I am developing a DCGAN using the this tutorial in PyCharm. As my usage of this tutorial suggests, I am quite new to DCGANs as I've previously only had a few experiences with machine learning ...
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Keras: Implementing paired Data Augmentation into Pix2Pix cGAN confusion

but learning GANs. Following a Jason Lee Brown tutorial (the code is his)... I am trying to add in Keras' ImageDataGenerator to the pix2pix algorithm. I can ...
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19 views

Reward Function for NLP

I would like to design an reward function , I am training two models from the first model that classify set of texts(paragraphs and keywords) and I also got some hidden states. The second model is ...
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Limits of generative models

If I was to train a pixel rnn, by concatenating images with noise vectors, I should be able to get new images from that distribution by starting on the prediction with a new noise vector. Would it be ...
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Why -logD trick in GANs training work?

When training the generator, we want to minimize log(1 - D(G(z))) (this has a form of log (1-x)). Using graph calculator we plot and see that the slope of this function is very small at first, thus ...
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How do Deep Auto-regressive models (like language generation models) take in 0-k previous tokens as input?

I'm new to deep auto-regressive architectures, and I am trying to build one for sequence generation. I've read a lot of papers about the theory behind these models, but I don't quite understand how a ...
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Is the hyperbolic tangent function a solution to the weight clipping problem of WGAN?

Instead of clipping to the range [-c,c] why not smoothly map into the range [-c,c] by using c*tanh(w) ? This would guarantee the Lipschizt constant is no greater than c. The problem I am talking ...
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How can I know my GAN does converge?

I am training my GAN, and by looking at the loss curves of the generator and discriminator, I think I am going on the right way because from this blog, my curves look reasonable: A stable GAN will ...
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1answer
184 views

Inception Score (IS) and Fréchet Inception Distance (FID), which one is better for GAN evaluation?

IS uses two criteria in measuring the performance of GAN: The quality of the generated images, and their diversity based on the entropy of the distribution of synthetic data. On the other hand, FID ...
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Instance segmentation and scene reconstruction

I've been recently interested in various segmentation tasks especially instance segmentation. Having been experimenting with various different datasets I stumbled upon using the method for indoor ...
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Is it possible to use a generative model to “share” private data?

Let's say we have some data set, with lots of instances $X$ and a target $y$. If it is of some importance, you may assume that it is a "real life" data set : medium sized, with important correlations, ...
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Reinforcement learning in bidirectional RNN

I have been self-learning deep generative neural network for a while. I am okay with the basics but I really need some guidance and jump start. I have recently came across this paper “Bidirectional ...
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Virtual Generation of Synthetic Ancient and Dirty English Documents

I have a collection of dirty background image, below is the sample: I have also a collection of an actual image of a dirty document with text on it, just like below: My problem with my actual image ...
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Keras Encoder - Decoder Model

Im training a model of 400 samples . The dataset contains 400 images of faces as input (X) and also 400 faces with glasses as output (Y) . im training the model by code below : ...
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GAN: Discriminator converges, generator learns almost nothing

In my GAN, the discriminator loss goes down steadily, while the generator loss oscillates / does not converge. I suspect this is due to the vanishing gradient problem. Theory: as the discriminator ...
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CycleGAN: Generator losses don't decrease, discriminators get perfect

So I´m training a CycleGAN for image-to-image transfer. The problem is: while the discriminator losses decrease, and are very small now, the generator losses don't decrease at all. The generator ...
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Implementing an RNN on multiple text sources

I want to implement an RNN to generate a new text based on many examples of existing texts of a certain format in the training data. The type of texts in the training data consists of 3 segments, ...
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Generative Model for learning Periodic solutions 3-body/N-body problems

I am tasked with finding research where a GAN or any other generative model is used to generate new shapes of 3-bodies moving under the influence of each others' gravitational pull, in a periodic ...
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How to supress previous results in a generative network?

Think of a generative network generating animal names. It's been trained on a set of animal names so this shouldn't be too hard. But say I want to generate 10 animal names. First I run the network ...
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1answer
28 views

Why joint probability in generative models?

I have been reading about generative models for past few days and there's one question that's bugging me. Many sources(Wiki, Google dev article) mention that generative models try to model the joint ...
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Could GANs be used to augment data?

I want to use GAN for data augmentation but I am confuse what are the pros. and cons. of data augmentation using GAN or why we use data augmentation using GAN compared to other data augmentation ...
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1answer
58 views

Are there some research papers about text-to-set generation?

I have googled but find no results. Text-to-(word)set generation or sequence-to-(token)set generation. For example, input a text and then output the tags for this text: ...
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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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1answer
76 views

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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1answer
60 views

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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1answer
71 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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5 views

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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1answer
59 views

Why are discriminative models denoted as $P\left(Y|X\right)$?

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
52 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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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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1answer
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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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1answer
238 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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367 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 ...