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- generative adversarial networks - Why use the output of the generator . . .
I believe the use of the generator output is to do with removing artifacting in the output, where say, 'truck like' images are recognized by the discriminator, but you want the discriminator to reject images which are 'truck like but have visual artifacts' What I don't understand is how why feeding the generator output in achieves this, other than, 'well, it seems to work pretty well in
- neural networks - Do GANs come under supervised learning or . . .
Explore related questions neural-networks terminology unsupervised-learning generative-adversarial-networks supervised-learning See similar questions with these tags
- generative adversarial networks - How to prevent vanishing exploding . . .
I am training several different GAN architectures, and I noticed that larger batch sizes may lead to vanishing or exploding gradients In the interest of accelerating training, however, larger batch
- generative adversarial networks - GAN with multiple discriminators . . .
MD-GAN (multi-Discriminator Generative Adversarial Networks for Distributed Datasets ) would be among the ones that you might be looking for It has been proposed a while ago now This has been proposed so that we can utilize high computation over distributed computing Most of the work has been done around multiple generators than on discriminators like Mc-GAN,S-GAN,Mg-GAN etc The probable
- training - What is Lipschitz constraint and why it is enforced on . . .
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
- generative adversarial networks - Is discriminator a regressor or . . .
Discriminator in the original GAN is a regressor No, it is a classifier It classifies an image as "real" or "fake", with the output usually being probability that the image is "real" (you could reverse this and use generated images as the target class, provided you change the generator training to match) Is it true with most of the (advanced or) contemporary GANs? In WGANs the W stands for
- generative adversarial networks - What is the intuition behind the . . .
From the previous paragraph: "This, however, can cause two problems First, it may result in over-fitting: if the model learns to assign full probability to the groundtruth label for each training example, it is not guaranteed to generalize Second, it encourages the differences between the largest logit and all others to become large, and this, combined with the bounded gradient ∂` ∂zk
- generative adversarial networks - Converting RGB images to Thermal . . .
I can convert to either near infrared spectrum images or far infrared spectrum image I am planing on using Generative networks for the task, specifically Pix2Pix For training GAN, there are datasets available with synchronized RGB and thermal image like dataset for MFNet
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