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Welcome to the Face-Generator-DCGAN wiki!

All of these faces are fake celebrities spawned by AI

One of the more unexpected outcomes of the contemporary AI boom is just how good these systems are at generating fake imagery. GANs are actually comprised of two separate networks: one that generates the imagery based on the data it’s fed, and a second discriminator network (the adversary) that checks if they’re real.

By working together, these two networks can produce some startlingly good fakes. And not just faces either — everyday objects and landscapes can also be created. The generator networks produces the images, the discriminator checks them, and then the generator improves its output accordingly. Essentially, the system is teaching itself.

In this project use of generative adversarial networks will be taken to generate new images of faces.

Dataset used- MNIST ( http://yann.lecun.com/exdb/mnist ) CelebA ( http://mmlab.ie.cuhk.edu.hk/projects/CelebA.html ) Since the celebA dataset is complex, MNIST was used to test before training on CelebA.

Input Examples-