Deep Convolutional Generative Adversarial Networks

Project description:

Input: The CIFAR10 dataset.

Generator: It takes a random vector from the latent space and map it to a synthesized image.

Discriminator: It maps the image to a binary score that is the probability of being fake/real image.

ooo

Goal: The model is trained by generating fake images produced by generator, mixing them with real images and adding a small random noise to their corresponding labels. Generator should be able to fool the discriminator.

The grid of decoded images are shown as follows which are difficult to distinguish: ooo

The source code of this project is available in my Github page.