We started this chapter by understanding the difference between generative and discriminative models. We learned that the discriminative models learn to find the good decision boundary that separates the classes in an optimal way, while the generative models learn about the characteristics of each class.
Later, we understood how GANs work. They basically consist of two neural networks called generators and discriminators. The role of the generators is to generate a new image by learning the real data distribution, while the discriminator acts as a critic and its role is to tell us whether the generated image is from the true data distribution or the fake data distribution, basically whether it is a real image or a fake image.
Next, we learned about DCGAN where we basically replace the feedforward neural networks in the generator and discriminator with convolutional neural...
