How does the discriminator work in GAN?
The discriminator in a GAN is simply a classifier. It tries to distinguish real data from the data created by the generator. It could use any network architecture appropriate to the type of data it’s classifying.
What does discriminator mean?
Definition of discriminator : one that discriminates especially : a circuit that can be adjusted to accept or reject signals of different characteristics (such as amplitude or frequency)
How to experiment with different learning rates for the discriminator and generator?
We can also experiment with different learning rates for the generator and the discriminator. For example, the following graph use the learning rate of 0.0003 for the discriminator and 0.0001 for the generator in the WGAN-GP training. Scale the image pixel value between -1 and 1. Use tanh as the output layer for the generator.
What is the difference between a discrminator and a generator?
The generator’s architecture can have a different number of layers, filters, and higher overall complexity. Figure 5: The architecture of the generator model showing each layer. Another main difference between the discriminator and the generator is the use of an activation function. The discrminator uses a sigmoid in the output layer.
What is the best way to optimize the GAN model?
Adam optimizer usually works better than other methods. Add noise to the real and generated images before feeding them into the discriminator. The dynamics of the GAN models are not well understood yet. So some of the tips are just suggestions and the mileage may vary.
What is Gan learning framework?
Figure 3: The GAN learning framework, which has the generator and the discriminator simultaneously trained. There are many architectures to consider for building the discriminator and the generator. We could build a deep neural network or Convolutional Neural Network (CNN) and some other options.