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Questions tagged [gan]

GAN refers to Generative Adversarial Networks. Such networks is made of two networks that compete against each other. The first one generates new samples and the second one discriminates between generated samples and true samples.

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What is the difference between ImageNet and ImageNet1k? How to download it?

Some papers mention just ImageNet and some papers mention ImageNet 1k database? What is the difference between these 2? Are they same or is the latter one subset of the former one? I'm working on ...
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Why Gaussian latent variable (noise) for GAN?

When I was reading about GAN, the thing I don't understand is why people often choose the input to a GAN (z) to be samples from a Gaussian? - and then are there also potential problems associated with ...
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Keras regularizers (kernel, bias and activity) vs tf.contrib.layers.apply_regularization

I have a DCGAN set up in tensorflow that is working well on the faces in the wild dataset. As an experiment, I tried using the same architecture in keras to better understand the difference in ...
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Why i get OOM error although my model is not that large?

I am a newbie in GPU based training and Deep learning models. I am running cDCGAN (Conditonal DCGAN) in tensorflow on my 2 Nvidia GTX 1080 GPU's. My data-set consists of around 32,0000 images with ...
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How can I enrich train data in case of cnn using target and time features

I have a sequence of images, let's say we ignore time specificity for now. In the other hand, target is a multivariate continuous time series. Let's consider it just a univariate one. Training a cnn ...
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What is the difference between TextGAN and LM for text generation?

I'm new to LeakGAN or SeqGAN or TextGAN. I know GAN is to generate text and let discriminator un-judge-able to real text and gen-text. LM(language model) is the task of predicting the next word and ...
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What is meant by Average Content Distance in Videos generated by GANs?

I'm reading a research paper on generating/synthesizing videos: MoCoGAN: Decomposing Motion and Content for Video Generation To evaluate the generated videos, they have used a metric called 'Average ...
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Deeplearning without an objective function?

In this article, the author talks about how deeplearning models no longer are trained for an objective function that humans specify, but find their own objective function. Specifically, he is talking ...
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Tensorflow Deep learning network not utilizing GPU?

I have a Nvidia GeForce GT 755M (PC), which I heard should be at least functional for running deep learning models. But when I train my model (DCGAN) and check the task manager process info (Win 10) I ...
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Why do most GAN (Generative Adversarial Network) implementations have symmetric discriminator and generator architectures?

For example, if the discriminator is a vanilla network of n layers, each with n(i) units, then, typically, the generator will also be a vanilla network of n layers, each with n(n-i) units (except the ...
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Are there any actual, commercial uses of GANs already?

Doing research on the internet, I found many scientific papers, ideas, and experiments concerning GANs. But I was unable to find a single example of it being already used commercially. Q1 can you ...
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In generative adversarial models (GANs), why should we solve min-max problem and not max-min?

I know that in GANs model, there is min-max game between generator and discriminator which discriminator tries to maximize the loss function and the goal of generator is to minimize it. But why we ...
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Can a Conditional GAN be used for a regression task, where the condition can take any continuous value?

I came across conditional GAN, where the classes can be provided as input to the GAN. I would like to know, if the same network can be used for classes which can take any continuous value.ie, the ...
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Dataset for GANs (logo generation)

I'm begining work on a new project that involves GANs. So far what I've learnt from some publications (e.g. this) is that these models require literally tonnes of images, e.g. 80K. The problem I'm ...
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Understanding distributions being concentrated on low dimensional manifold (as to GAN convergence)

I am working through some papers concerning convergence of Generative Adversarial Networks (GANs) as in Arjovsky (2017) and I have trouble understanding how to imagine a distribution being ...
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How to train the generator in a recurrent GAN (Keras)

I am trying to train a Recurrent GAN that is meant to generate geospatial movement data (sequences of 3-tuples of latitude, longitude and time). You may simply consider it a sequences of vectors with ...
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how to implement infoGAN's loss function in Keras' functional API

I have been trying to make an infoGAN based on this, but it is in pure tensorflow and I can't really figure out how I would implement the Q_loss in Keras (preferably the functional API). here is ...
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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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Confused about transpose convolution and tensor shapes in tensorflow GAN tuturial

https://github.com/tensorflow/tensorflow/blob/r1.11/tensorflow/contrib/eager/python/examples/generative_examples/dcgan.ipynb class Generator(tf.keras.Model): def init(self): super(Generator, ...
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Value Function of Generative Adversarial Network

My question concerns the notation used in the Value function of a GAN Does "x ~ p_data(x)" mean $$ E_{x\sim p_{data}}[log(D(x)]= \sum_{x} logD(x)(x)p_{data}(x)$$ ?? Many thanks,
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How to calculate $\phi_{i,j}$ in VGG19 network?

In the paper Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network by Christian Ledig et al., the distance between images (used in the loss function) is calculated from ...
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Can I use a GAN to increase my Dataset used for Image detection?

I am currently working on a machine learning project where I use the YOLO Algorithm to detect an object inside of a picture or video. The problem I face is that the specific image set (side-scan sonar)...
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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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Architecture Advice for training a GAN

I'm trying to create a model that generates worlds for a game. The game is 2-dimensional and small worlds sizes. I've sampled about 15,000 worlds that are about 200x200 blocks. Each block has its own ...
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Fully connected layer output explodes, but weights, gradients, and inputs all have sane values

I'm trying to train a GAN, and the architecture includes a fully connected layer before the output activation function. In my case, by the second training iteration this layer's output always explodes....
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Is it possible for a DCGAN to do regression? What are some examples of this?

I'm currently a student doing some machine learning projects, and I want to use generative adversarial networks to train some data to discern for example, how old someone is. The intended output is a ...
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Why use GAN in NLG?

I am interested in the GAN recently. There are many papers that recently applied GAN to NLG. I do not know much about NLP or NLG, but I wonder why I use GAN for NLG. For better quality? Or for many ...
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286 views

Gumbel Softmax vs Vanilla Softmax for GAN training

When training a GAN for text generation, i have seen many people feeding the gumbel-softmax from the generator output and feed into the discriminator. This is to bypass the problem of having to sample ...
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How to collect all variables as a list in tensorflow grouped as a function

I am trying to reproduce the cGAN network architecture introduced on the recent paper deep video portrait(2018, Standford) I have defined Generator as ...
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GAN's for data augmentation [closed]

I am working to augment my data using Generative Adversarial Networks, I have used Deep Convolutional GAN's for this purpose but they are not learning the right data distribution, so please suggest me ...
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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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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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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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Can GAN generate very happy facial expression

I have some dataset ${(x1,y1), (x2,y2)...(xn,yn)}$, where, $x$ is the picture of a facial expression,while $y$ is the fraction corresponding to their degree of the happiness (happy laugh: $y$ close to ...
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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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GAN vs DCGAN difference

I am trying to understand the key difference between GAN and DCGAN. I know that DCGAN uses a convolutional network. But: What data is better to push into GAN and what data fits better to DCGAN? ...
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GAN to generate a custom image does not work

I have been training a GAN in the cloud for some time now. I use Google's free credit. My laptop with a CPU doesn't seem to be up to the task. The image I want to generate is this. Even though the ...
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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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Could someone explain to me how back-prop is done for the generator in a GAN?

I'm not very familiar with neural networks, however, I though I understood the concept of back propagation as starting from the error in the output layer. Say, we have 3 neurons in the output layer ...
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GANs and grayscale imagery colorization

I am currently studying colorization of grayscale satellite imagery as part of my Master's internship. After looking for various machine learning techniques, I quickly decided to go for deep learning, ...
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How can I train Generative Adversarial Inverse Reinforcement Learning(GAIL) by feeding encoded state representations in the GAN architecture ?

In GAIL there is an inside step where we need to train a GAN. When training GAN we input expert's [State, Action] pair and Agent's predicted [State, Action] pair into the Discriminator. Let's say my ...
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GAN - why doesn't the generator nullify the noise input?

In GAN architecture, during training, what keeps the generator's output dependant on the input noise? Why don't the weights of the noise input become zero (plus a bias)? I would expect the generator ...
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365 views

WGAN is too slow what are some ways to tweak for speed ups?

I have implemented a vanilla GAN which gave good results very fast but it had a lot of mode collapse issue, because of this I learned about WGAN which suppose to fix this, in fact they claim they have ...
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How to better visualizing Earth Mover Distance in relation with generative adversarial networks?

After reading the original EMD paper from 1998 I am having a hard time trying to visualize the connections between their dirt pile example to generative adversarial networks. Everything is kind of ...
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Is there any applications in Generative Adversarial Inverse Reinforcement Learning in real world problems?

I think IRL can be super awesome in understanding behavioral patterns, reason out activities etc. But when it comes to applying to real-world problems still IRL suffers dues to unknown dynamics and ...
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how to generate automatically images meshing up different shapes with a deep learning software?

My pursuite is to generate something like a grottesque(a kind of painting producing human-animals and plants hybrids). I need to do something like this paints in order to create an art exhibition. I ...
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Image to image translation from image to sketch using GANs, do I need to annotated the face at all?

So far I have read 2 papers on GANs and it seems like they are unsupervised networks that only uses supervision for the discriminator. If I want to translate an image from a picture to line art all I ...
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216 views

Generator loss not decreasing- text to image synthesis

I am implementing Scott Reed's paper on Generative Adversarial Text to Image Synthesis.(https://arxiv.org/pdf/1605.05396.pdf) The dataset I am using is a simple one, consisting of images of circles, ...
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360 views

GAN discriminator converging to one output

I am running a GAN on the MNIST dataset. As the GAN continues to train, the quality of the generator seems to get worse, and it even seems as if it is starting to converge to just one value. Epoch 7:...
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feature selection such that features that explain target but are not correlated with confounds are picked

Please suggest a feature selection technique which selects features such that they explain target well but are not correlated with given know confounds. This paper(section 2.3) suggests using a GAN ...