Adversarial Discriminative Domain Adaptation Adversarial Discriminative Domain Adaptation
Paper summary _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.

Summary by Léo Paillier 11 months ago
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