Using generative adversarial networks to generate new images of faces (datasets: MNIST, CelebA).

Generative Adverserial Network for face generation using Tensorflow and DCGAN architecture

Face generation using generative adversarial networks to generate new images

Use GANs with normalization techniques like dropouts, batch normalization along with having a low variance in kernel weight initialization, achieve realistic images of faces trained on the CelebA dataset. Images also have been generated of hand written digits after being trained on the MNIST dataset. This would be useful for generating training …