Simon Osindero, Mehdi Mirza - 2014
Publications: arXiv Add/Edit
Generative Adversarial Nets  were recently introduced as a novel way to train generative models. In this work we introduce the conditional version of generative adversarial nets, which can be constructed by simply feeding the data, y, we wish to condition on to both the generator and discriminator. We show that this model can generate MNIST digits conditioned on class labels. We also illustrate how this model could be used to learn a multi-modal model, and provide preliminary examples of an application to image tagging in which we demonstrate how this approach can generate descriptive tags which are not part of training labels.
The repository contains example code for conditional Generative Adversarial Model in theno and Lasagne. The code follows the paper https://arxiv.org/abs/1411.1784 but with a different deep architecture. The folder Generative Images contains Some sampled generated images from the code.