Questions tagged [generative-adversarial-networks]

For questions related to generative adversarial networks (GANs), introduced in the paper Generative Adversarial Nets (2014) by J. Goodfellow et al. A GAN is composed of a discriminative model (D) and a generative model (G). The discriminator D needs to distinguish between data generated by the generator G and data in the training set, while the generator G needs to generate data such that the discriminator D is not able to accomplish its task.

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19 views

Are there architectures to generate pictures from four labels? (VAEs, GANs)

I want to try something with image creation via NNs. I have come across Variational Autoencoders and Generative Adversarial Networks as possible solutions but have only found image creatinon with ...
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Decision boundary figure in Least square GAN paper

I currently reading Least Square GAN paper. But, I cannot interpret the one of the its figures. . Explanation of the figure goes like this: Figure 1: Illustration of different behaviors of two loss ...
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Is it possible to use an internal layer's outputs in a loss function?

For a network of the form: Input(10) Dense(200) Dense(100+10) Dense(20) Output() Those +10 outputs are what I want to add to ...
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Feasibility f using Word2Vec embeddings as input for GANs

If I have a dataset of artwork and a list of say, 10-12 words that describe each piece of artwork, how feasible would it be to convert those words into a word2vec embedding and use that (plus lets say ...
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How does the output distribution of a GAN change if the parameters are slightly purturbed?

Suppose $G_{\phi}:\mathcal{Z}\rightarrow \mathcal{X}$ is a generator (neural network, non-invertible) that can sample from some distribution $\pi$ on $\mathcal{X}$. That is, $G_{\phi}(z)\sim \pi$ when ...
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Do dataset sizes matter in a Style GAN?

When working with classifiers, a class imbalance is a huge issue for our models. If we have too many images of class 1 and too few images from ...
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SAGAN - is there a mistake in the original paper?

in the original paper the following scheme of the self-attention appears: https://arxiv.org/pdf/1805.08318.pdf In a later overview: https://arxiv.org/pdf/1906.01529.pdf this scheme appears: ...
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Why does this paper say that the Nash-equilibrium of GAN is given by a discriminator which is 0 everywhere on the data distribution?

I am facing difficulty in understanding the bolded portion of the following statement from this paper GANs are defined by a min-max two-player game between a discriminative network $D_\Psi(x)$ and ...
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25 views

What to do with a GAN that trained well but got worse over time?

I am training a WGAN-GP network based on the following paper, though I am using a different dataset. Now, for the first ~ 60-70 epochs, my network trained really well, which I could see in the loss ...
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IRGAN and Gumbel-Softmax Distribution

I want to adapt the IRGAN model so that it uses the Gumbel-Softmax distribution for sampling the probabilities to pick documents from rather than having to use the annealed softmax. I studied the ...
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Why do we sample vectors from a standard normal distribution for the generator?

I am new to GANs. I noticed that everybody generates a random vector (usually 100 dimensional) from a standard normal distribution $N(0, 1)$. My question is: why? Why don't they sample these vectors ...
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GAN Generator Output w/ Periodic Noise

I am training a Semi-Supervised GAN, using multivariate time-series with window of shape (180*80) with the generator and discriminator architecture below. My data is scaled using Robust Scaler, so I ...
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Classifying generated samples with Wasserstein-GAN as real or fake

I'm quite new to GANs and I am trying to use a Wasserstein GAN as an augmentation technique. I found this article https://www.sciencedirect.com/science/article/pii/S2095809918301127, and would like to ...
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Why do the inception score and the Fréchet inception distance use the inception network and not another network?

So I was researching about the evaluation of GANs and found these two metrics which seem to be the most popular. I understand that the main ideia is to apply the data to a pre-trained network in order ...
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Best Machine Learning Model for “Predicted” Image Generation

I am currently working on undergraduate research to determine hotspots for hand-surface contact. Ideally, I would like to give the model a depth image as input: Example of synthetic depth image and ...
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Why does the relativistic discriminator increase the probability that generated data are real and decrease the probability that real data are real?

I was reading the ESRGAN whitepaper, where I came across this line: Relativistic discriminator [2] is developed not only to increase the probability that generated data are real but also to ...
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Is the GAN architecture better suited for medical image denoising than the CNN?

I'm considering using GANs for medical image denoising, based on previous literature, like this and this. My input to the GAN would be a high-noise image and my ideal output would be a low-noise, high-...
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Where can I find a progressively trained GAN's pretrained low-resolution models?

StyleGAN is trained progressively, meaning that it starts as a small network trained to produce 4x4 images, then a layer is added which doubles the resolution to 8x8, then 16x16, etc. The final result ...
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Why is my GAN more unstable with bigger networks?

I am working with generative adversarial networks (GANs) and one of my aims at the moment is to reproduce samples in two dimensions that are distributed according to a circle (see animation). When ...
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Why don't those developing AI Deepfake detectors use two detectors at once so as to catch deepfakes in one or the other?

Why don't those developing AI Deepfake detectors use two differently trained detectors at once that way if the Deepfake was trained to fool one of the detectors the other would catch it and vice-versa?...
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Aren't scores in the Wasserstein GAN probabilities?

I am quite new to GAN and I am reading about WGAN vs DCGAN. Relating to the Wasserstein GAN (WGAN), I read here Instead of using a discriminator to classify or predict the probability of generated ...
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What dataset might Elon Musk's Dall-E have used?

Dall-E, it can generate many imaginative images from the description, even some peculiar images, how did they actually create this kind of dataset to train this AI , because there is not much of that ...
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In this implementation of pix2pix, why are the weights for the discriminator and generator losses set to 1 and 100 respectively?

I am working on a pix2pix GAN model that was inspired by the code in this Github repository. The original code is working and I have already customized most of the code for my needs. However, there is ...
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What are the fundamental differences between VAE and GAN for image generation?

Starting from my own understanding, and scoped to the purpose of image generation, I'm well aware of the major architectural differences: A GAN's generator samples from a relatively low dimensional ...
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Parametrizing non-analytical functions using generative models

My questions centers around what method is best to use parametrize a response function for an experiment. We are currently using ab initio simulation to model our experiment's response. Unfortunately, ...
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How is the latent vector transforming to a feature map in DCGAN (Generator structure)?

I'm working on the code trying to generate new images using DCGAN model. The structure of my code is from the PyTorch tutorial here. I'm a bit confused trying to find and understand how the latent ...
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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)] \\ & =...
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Can someone explain R1 regularization function in simple terms?

I'm trying to understand the R1 regularization function, both the abstract concept and every symbol in the formula. According to the article, the definition of R1 is: It penalizes the discriminator ...
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45 views

Optimum Discriminator for label smoothed GAN

I was reading the paper called Improved Techniques for Training GANs. And, in the one-sided label smoothing part, they said that optimum discriminator with label smoothing is $$ D^*(x)=\frac{\alpha \...
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56 views

Why is it easier to construct adversarial examples relative to training neural networks?

I was having looking at this lecture by Ian Goodfellow and my doubt is around 18:00 timestamp where he explains generation of adversarial examples using FGSM. He mentions that the there is a linear ...
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Wasserstein GAN: Implemention of Critic Loss Correct?

The WGAN paper concretely proposes Algorithm 1 (cf. page 8). Now, they also state what their loss for the critic and the generator is. When implementing the critic loss (so lines 5 and 6 of Algorithm ...
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Recent deep learning textbook (i.e. covering at least GANs, LSTM and transformers and attention)

I am searching for an academic (i.e. with maths formulae) textbook which covers (at least) the following: GAN LSTM and transformers (e.g. seq2seq) Attention mechanism The closest match I got is Deep ...
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Hairstyle Virtual Try On

I want to help people with cancer who are under chemotherapy, and generally people who have lost their hair to Virtually Try-On Toupees/Wigs on their head. VTO must support both the frontal and side ...
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Can I start with perfect discriminator in GAN?

In many implementations/tutorials of GANs that I've seen so far (e.g. this), the generator and discriminator start with no prior knowledge. They continuously improve their performance with training. ...
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How does noise input size affect fake image generation with GANs?

In Generative Adversarial Networks, the Generator takes noise vector as input and feeds it forward to create an image. The noise vector consists of random numbers sampled from the normal distribution. ...
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1answer
37 views

What do they mean by “contradictory loss”?

In page 4 of the paper https://arxiv.org/pdf/2009.07047v1.pdf, it says the encoder $E_{R,X}$ of $VAE_1$ tries to fool the discriminator with a contradictory loss to ensure that $R$ and $X$ are mapped ...
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Why using Aux() to implement VAE_1?

I have a bit of difficulties working on the article :https://arxiv.org/pdf/2009.07047v1.pdf. I want to implement the VAE_1, but it used VAE-GAN. I am familiar with ...
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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 ...
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1answer
28 views

Why do we need to provide false labels to the discriminator on purpose to train GANs?

This is the tutorial that I used to learn about GANs. In this tutorial, it taught us to intentionally provide false labels to "fool" the discriminator, but does it make the discriminator ...
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1answer
32 views

GAN model predictions before training is predictable

I have a dataset of 3000 8x8 images, and I would like to train a GAN for an image generation purpose. I am planning to start with a simple GAN model and see if it overfits. Before training, I try to ...
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1answer
56 views

Explain the difference in graphical patterns between discriminator fake loss and generator loss in GAN

In GAN (generative adversarial networks), let us take "binary cross-entropy" as the loss function for discriminator $$(overall \; loss = -\sum log(D(x_i)) -\sum log(1-D(G(z_i))) $$ $$ where \...
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1answer
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In the MINE paper, why is $\hat{G}_B$ biased, and how does the exponential moving average reduce the bias?

While reading the Mutual Information Neural Estimation (MINE) paper [1] I came across section 3.2 Correcting the bias from the stochastic gradients. The proposed method requires the computation of the ...
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WGAN-GP Loss formalization

I have to write the formalization of the loss function of my network, built following the WGAN-GP model. The discriminator takes 3 consecutive images as input (such as 3 consecutive frames of a video) ...
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93 views

SeqGAN - Policy gradient objective function interpretation

Could someone clear my doubt on the loss function used in SeqGAN paper . The paper uses policy gradient method to train the generator which is a recurrent neural network here. Have I interpreted the ...
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Wasserstein GAN with gradient penality - Loss values

I have trained a WAN with gradient penalty and the loss values ​​seem to me much higher than the examples I have seen on the net. The generator receives 2 images as input and must generate a ...
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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 ...
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2answers
87 views

What is the right way to train a generator in a GAN?

I am not fully understanding how to train a GAN's generator. I have a few questions below, but let me first describe what I am doing. I am using the MNIST dataset. I generate a batch of random images ...
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GAN for specific face attribute modification

A recent paper "MagGAN High Resolution Face Attribute Editing with Mask Guided GAN" published this month (October 2020) describe how an approach has been developed to deal with specific face ...
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How to optimize my GAN generator and discriminator models' structures?

I'm using Tensorflow to feed a DCGAN 3000 320x320 colored images of cars. The goal is to generate new cars. I've been training on Google Colab for the past 10 hours or so. I guess I can expect results ...
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Why scaling down the parameter many times during training will help the learning speed be the same for all weights in Progressive GAN?

The title is one of the special things in Progressive GAN, a paper of the NVIDIA team. By using this method, they introduced that Our approach ensures that the dynamic range, and thus the learning ...