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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 using a GAN with small networks (3 layers with 50 neurons each), the results are more stable than with bigger layers (3 layers with 500 neurons each)
  • deep learning - How are generative adversarial networks trained . . .
    That said, using this scenario could be a good "unsupervised" way to improve the classification power of neural networks, as it forces the generator model to learn better features of real data, and to learn how to distinguish between actual features and noise, using much less data that is needed for a traditional supervised learning scheme
  • How can I use Generative Adversarial Networks to solve the imbalanced . . .
    $\begingroup$ I doubt it is possible, but I have not read the work on Conditional GANs I suspect that about the only thing you would achieve is some regularisation (which may help prevent over-fitting), but other regularisation techniques would be easier to implement than using generative models in your case IMO
  • 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)
  • generative adversarial networks - How is the output of the Generator in . . .
    If you ask this question it means you conceive a generative adversarial network as a combination of 2 separate entities, the discriminator and generator, but this is not really the case It is true that for convenience we distinguish between generator and discriminator since they fulfill separate purposes, but by simply looking at a drawing of
  • generative adversarial networks - Is it feasible to use GAN for high . . .
    Generative Adversarial Networks, basically boil down to a combination of a generic Generator and a Discriminator trying to beat each other, so that the generator tries to generate much better images (usually from noise) and discriminator becomes much better at classification
  • generative adversarial networks - Why use the output of the generator . . .
    generative-adversarial-networks; Share Improve this question Follow edited May 19, 2019 at 4:26 Doug
  • generative adversarial networks - GANs inputs normalized and generator . . .
    Is it possible that the discriminator picks on this phenomenon? It's not just possible, it's a certainty The generator should learn to translate an input distribution A to an output distribution B, if distribution B has range $(-\infty, \infty)$ and your generator can output only values in range $(-1, 1)$ the translation between the two simply can't happen
  • What are the fundamental differences between VAE and GAN for image . . .
    How important is it that the generator of a generative adversarial network doesn't take in information about input classes? 3 Why use the output of the generator to train the discriminator in a GAN?





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