_Objective:_ Define a framework for Adversarial Domain Adaptation and propose a new architecture as state-of-the-art.

 _Dataset:_ MNIST, USPS, SVHN and NYUD.   

## Inner workings:

Subsumes previous work in a generalized framework where designing a new method is now simplified to the space of making three design choices:

*   whether to use a generative or discriminative base model.
*   whether to tie or untie the weights.
*   which adversarial learning objective to use.

[![screen shot 2017-04-18 at 5 10 01 pm](https://cloud.githubusercontent.com/assets/17261080/25138167/15d5e644-245a-11e7-9fb8-636ce4111036.png)](https://cloud.githubusercontent.com/assets/17261080/25138167/15d5e644-245a-11e7-9fb8-636ce4111036.png)

## Architecture:

[![screen shot 2017-04-18 at 5 14 44 pm](https://cloud.githubusercontent.com/assets/17261080/25138526/07848bd0-245b-11e7-94c9-f6ae7ccea76f.png)](https://cloud.githubusercontent.com/assets/17261080/25138526/07848bd0-245b-11e7-94c9-f6ae7ccea76f.png)

## Results:

Interesting as the theoretical framework seem to converge with other papers and their architecture improves on previous papers performance even if it's not a huge improvement.