Summary
GAN is a powerful method for generating not only images but also paintings, and even 3D objects (using newer variants of a GAN). We saw how, using a combination of discriminator and generator networks (each with five convolutional layers), we can start with random noise and generate an image that mimics real images. The play-off between the generator and discriminator keeps producing better images by minimizing the loss function and going through multiple iterations. The end result is fake pictures that never existed in real life.
It's a powerful method, and there are concerns about its ethical use. Fake images and objects can be used to defraud people; however, it also creates endless new opportunities. For example, imagine looking at a picture of fashion models while shopping for a new outfit. Instead of relying on endless image shoots, using a GAN (and DCGAN), you can generate realistic pictures of models with all body types, sizes, shapes, and colors, helping both...