Thursday, January 23, 2020


Here we have 2 neural networks GENERATOR and  DISCRIMINATOR


  • Is a simple classifier that tries to classify the images as real from the training set or fake generated images.

  • Acting as an adversary to the discriminator.
  • It aims to trick the discrminator, giving it generated images that look as if they've come from the training set.
  • If the generator produces an image that the discriminator thinks is fake, then it would change it behavior and try again.
  • Generator would trick till the discrimnator starts thinking that its generated data comes from the real training set.( Goal is to force the discriminator to have a high error rate as possible.Note that at the same time discriminator is also training. it looks at examples of real and fake images and getting better at recognizing differences between them.its goal is to have a low error rate.)
Task is to have both discriminator and generator networks with opposing goals.We define opposing generator and discriminator loss functions.
At the end of the training discriminator should not be able to tell the difference between real and fake images

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