# Conditional Generative Adversarial Nets

## Introduction

* Conditional version of [Generative Adversarial Nets (GAN)](https://gist.github.com/shagunsodhani/1f9dc0444142be8bd8a7404a226880eb) where both generator and discriminator are conditioned on some data **y** (class label or data from some other modality).
* [Link to the paper](https://arxiv.org/abs/1411.1784)

## Architecture

* Feed **y** into both the generator and discriminator as additional input layers such that **y** and input are combined in a joint hidden representation.

## Experiment

### Unimodal Setting 

* Conditioning MNIST images on class labels.
* *z* (random noise) and **y** mapped to hidden layers with ReLu with layer sizes of 200 and 1000 respectively and are combined to obtain ReLu layer of dimensionality 1200.
* Discriminator maps *x* (input) and **y** to maxout layers and the joint maxout layer is fed to sigmoid layer.
* Results do not outperform the state-of-the-art results but do provide a proof-of-the-concept.

### Multimodal Setting

* Map images (from Flickr) to labels (or user tags) to obtain the one-to-many mapping.
* Extract image and text features using convolutional and language model.
* Generative Model
    * Map noise and convolutional features to a single 200 dimensional representation.
* Discriminator Model
    * Combine the representation of word vectors (corresponding to tags) and images.

## Future Work

* While the results are not so good, they do show the potential of Conditional GANs, especially in the multimodal setting.