So for, we have seen how GAN can be used to generate realistic images. In this section, we will see how GAN can be used for classification tasks where we have less labeled data but still want to improve the accuracy of the classifier. Here we will also use the same Street View House Number or SVHN dataset to classify images. As previously, here we also have two networks, the generator G and discriminator D. In this case, the discriminator is trained to become a classifier. Another change is that the output of the discriminator goes to a softmax function instead of a sigmoid function, as seen earlier. The softmax function returns the probability distribution over labels:
Now we model the network as:
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