Face Generation based on Race using Generative Adversarial Networks

The field of image generation has seen immense amounts of improvement, with the advent of generative adversarial networks, an application of unsupervised learning. In this paper, the images are generated using Generative Adversarial Networks with the help of two sets of data, one containing the Asian faces and the other that holds Caucasian faces. Deep Convolutional Generative Adversarial Networks, that use convolutional neural networks for the generator, and the discriminator, have been utilized in order to accomplish the task of the generation due to advantages over conventional GANs such as less noise, greater stability of results.