The discriminator is a classification neural network and we can train it in the usual way, using gradient descent and backpropagation. However, the training set is composed of equal parts real and generated samples. Let's see how to incorporate that in the training process:
- Depending on the input sample (real or fake), we have two paths:
- Select the sample from the real data, , and use it to produce .
- Generate fake sample, . Here, generator and discriminator work as a single network. We start with a random vector, ...