Multiple Generative Adversarial Networks (GANs) implemented in PyTorch and Tensorflow.
Check out this blog post for an introduction to Generative Networks.
Vanilla GANs found in this project were developed based on the original paper Generative Adversarial Networks by Goodfellow et al.
These are trained on the MNIST dataset, and learn to create hand-written digit images using a 1-Dimensional vector representation for 2D input images.
MNIST-like generated images before & after training.
Deep Convolutional Generative Adversarial Networks (DCGANs) in this repository were developed based on the original paper Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks by Radford et al.
These are trained on the CIFAR10 and the MNIST datasets. They use 3 dimensional representations for images (length x height x colors) directly for training.
CIFAR-like generated images before & after training.