Questions tagged [discriminator]
For questions related to the discriminator component of GANs.
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Does the Weights of Discriminator get updated when traning Generators in GANs?
When we train the GAN we usually train the discriminator first then the generator, first we stop the generator from updating its weight by removing it from the computation graph, using fake_image....
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What is the domain of the discriminator of a GAN?
I've read that the discriminator $D$ validates an image $D(x)$, where $x$ is either a real image or a fake one created by the generator, i.e. $ D(G(x))$.
What does the function of the discriminator ...
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Why do we train the discriminators k times but train the generator only 1 time in a iteration in GAN?
In this paper https://arxiv.org/abs/1406.2661 , the codes for training a gan are :
Why do we train the discriminator for $k$ steps while the generator only for $1$ step? Why not the other way around?
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Is discriminator a regressor or classifier in implementations?
GAN has two components: generator and discriminator.
Discriminator in the original GAN is a regressor and always gives value in $[0, 1]$. You can read it in original paper
$D(x)$ represents the ...
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What is Lipschitz constraint and why it is enforced on discriminator?
The following is the abstract for the research paper titled Improved Training of Wasserstein GANs
Generative Adversarial Networks (GANs) are powerful generative models,
but suffer from training ...
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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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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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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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Training Conditional DCGAN with GAN-CLS loss
I am trying to implement conditional GAN using GAN-CLS loss as described in paper: https://arxiv.org/abs/1605.05396
So, while training discriminator, I should I have three batches of data:
[...
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GANs: Should Generator update weights when Discriminator says false continuously
My GANs is like this:
Train an autoencoder (VAE), get the decoder part and use as Generator
Train Discriminator
After training, do the generation in these steps:
Call Generator to generate an image
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Can some one help me understand this paragraph from Nvidia's progressive GAN paper?
In the paper Progressive growing of gans for improved quality, stability, and variation (ICLR, 2018) by Nvidia researchers, the authors write
Furthermore, we observe that mode collapses traditionally
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