The idea of feature matching is to add an extra variable to the cost function of the generator in order to penalize the difference between absolute errors in the test data and training data.
In this section, we explain how to use GAN to build a classifier with the semi-supervised learning approach.
In supervised learning, we have a training set of inputs X and class labels y. We train a model that takes X as input and gives y as output.
In semi-supervised learning, our goal is still to train a model that takes X as input and generates y as output. However, not all of our training examples have a label y. ;
We use the SVHN dataset. We'll turn the GAN discriminator into an 11 class discriminator (0 to 9 and one label for the fake image). It will recognize the 10 different classes of real SVHN digits, as well as an eleventh class of fake images that come from the generator. The discriminator will get to train on real labeled images...
