When to penalize the discriminator in Gan?
The optimization may turn too greedy and produces no long term benefit. In GAN, overconfidence hurts badly. To avoid the problem, we penalize the discriminator when the prediction for any real images go beyond 0.9 ( D (real image)>0.9 ). This is done by setting our target label value to be 0.9 instead of 1.0. Here is the pseudo code:
Which is the best way to improve Gan performance?
The means of the real image features are computed per minibatch which fluctuate on every batch. It is good news in mitigating the mode collapse. It introduces randomness that makes the discriminator harder to overfit itself. Feature matching is effective when the GAN model is unstable during training.
How does deep learning avoid overconfidence in Gan?
To mitigate the problem, deep learning uses regulation and dropout to avoid overconfidence. In GAN, if the discriminator depends on a small set of features to detect real images, the generator may just produce these features only to exploit the discriminator. The optimization may turn too greedy and produces no long term benefit.
Is it possible to reach zero loss for both generator and discriminator?
It is impossible to reach zero loss for both generator and discriminator in the same GAN at the same time. However, the idea of the GAN is not to reach zero loss for any of the game agents (this is actually counterproductive), but to use that “double gradient descent” to “converge” the distribution of G (z) to the distribution of x.
How are sample and random noise used in Gan?
Define two functions to provide a true sample and random noise. The true sample trains the discriminator, the random noise feeds the generator. The generator is trained to output means that match our desired distribution. This is a pretty simple 4 layer network, takes in noise and produces an output.
Are there any problems with unrolled Gans?
Unrolled GANs: Unrolled GANs use a generator loss function that incorporates not only the current discriminator’s classifications, but also the outputs of future discriminator versions. So the generator can’t over-optimize for a single discriminator.
What do you need to know about Gan?
Usually you want your GAN to produce a wide variety of outputs. You want, for example, a different face for every random input to your face generator. However, if a generator produces an especially plausible output, the generator may learn to produce only that output.