_Objective:_ In an unconditional GAN it's not possible to control the mode of the data being generated which is what this paper tries to accomplish using the label data (but it can be generalized to any kind of conditional data).

_Dataset:_ [MNIST](yann.lecun.com/exdb/mnist/) and [MIRFLICKR](http://press.liacs.nl/mirflickr/).


#### Inner workings:

Changes the loss to the conditional loss:  
[![screen shot 2017-04-24 at 10 07 25 am](https://cloud.githubusercontent.com/assets/17261080/25327832/e86f53fe-28d5-11e7-8694-6df8f2e1ef18.png)](https://cloud.githubusercontent.com/assets/17261080/25327832/e86f53fe-28d5-11e7-8694-6df8f2e1ef18.png)

For implementation the only thing needed is to feed the label data to both the discriminator and generator:  
[![screen shot 2017-04-24 at 10 07 18 am](https://cloud.githubusercontent.com/assets/17261080/25327826/e53ab4a8-28d5-11e7-8056-1518602d50c9.png)](https://cloud.githubusercontent.com/assets/17261080/25327826/e53ab4a8-28d5-11e7-8056-1518602d50c9.png)

#### Results:

Interesting at the time but not surprising now. There's not much more to the paper than what is in the summary.