<commit_before>---
layout: page
title: Adversarial Networks
permalink: /adversarial
hide: true
---

## Instructor

| ![<NAME>][KevinMcGuinness-photo]  |
|:-:|
|  [<NAME> (KM)](KevinMcGuinness-web)     |

[KevinMcGuinness-web]: https://www.insight-centre.org/users/kevin-mcguinness
[KevinMcGuinness-photo]: img/instructors/KevinMcGuinness.jpg "<NAME>"

* [Slides](slides/D4L1-adversarial.pdf)

## Videos

* Sirajology, ["Generative Adversarial Nets"](https://www.youtube.com/watch?v=deyOX6Mt_As). Fresh Machine Learning, July 2016. 

## Tutorials

* <NAME>, <NAME>, <NAME>, <NAME>, <NAME>, <NAME>, <NAME>, <NAME>, <NAME>, <NAME>, <NAME>, <NAME>, <NAME>, AND <NAME>, ["Generative models"](https://openai.com/blog/generative-models/). OpenAI 2016.

* O'Shea Research, [MNIST Generative Adversarial Model in Keras](https://oshearesearch.com/index.php/2016/07/01/mnist-generative-adversarial-model-in-keras/). July 2016.

* <NAME>, ["Image Completion with Deep Learning in TensorFlow"](http://bamos.github.io/2016/08/09/deep-completion/). 2016.


## Related Work & Resources

* Szegedy, Christian, <NAME>, <NAME>, <NAME>, <NAME>, <NAME>, and <NAME>. ["Intriguing properties of neural networks."](http://arxiv.org/abs/1312.6199) arXiv preprint arXiv:1312.6199 (2013). [[UPC slides](https://docs.google.com/presentation/d/1g58oIdS_a9uEeiuDHC85OrU2tql3Prrn_jcHCPjB6pU/edit?usp=sharing)]

* [Practical Black-Box Attacks against Deep Learning Systems using Adversarial Examples](http://arxiv.org/abs/1602.02697)

* Im, <NAME>, <NAME> Kim, <NAME>, and <NAME>. ["Generating images with recurrent adversarial networks."](http://arxiv.org/abs/1602.05110) arXiv preprint arXiv:1602.05110 (2016). [[software](https://github.com/jiwoongim/GRAN)]

* Radford, Alec, <NAME>, and <NAME>. ["Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks."](http://arxiv.org/abs/1511.06434) arXiv preprint arXiv:1511.06434 (2015). [[Theano](https://github.com/Newmu/dcgan_code)] [[TensorFlow](https://github.com/jazzsaxmafia/dcgan_tensorflow)] [[reddit](https://www.reddit.com/r/MachineLearning/comments/3tykrw/unsupervised_representation_learning_with_deep/)]

* Dumoulin, Vincent, <NAME>, <NAME>, <NAME>, <NAME>, <NAME>, and <NAME>. ["Adversarially Learned Inference."](https://ishmaelbelghazi.github.io/ALI/) arXiv preprint arXiv:1606.00704 (2016).

* Donahue, Jeff, <NAME>, and <NAME>. ["Adversarial Feature Learning."](http://arxiv.org/abs/1605.09782) arXiv preprint arXiv:1605.09782 (2016).

* Goodfellow, Ian, <NAME>, <NAME>, <NAME>, <NAME>, <NAME>, <NAME>, and <NAME>. ["Generative adversarial nets."](http://papers.nips.cc/paper/5423-generative-adversarial) In Advances in Neural Information Processing Systems, pp. 2672-2680. 2014.

* Radford, Alec, <NAME>, and <NAME>. ["Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks."](http://arxiv.org/abs/1511.06434) International Conference on Learning Representations (2016). [[software](https://github.com/Newmu/dcgan_code)] [[software Theano](https://github.com/mikesj-public/dcgan-autoencoder)] [[software TensorFlow](https://github.com/carpedm20/DCGAN-tensorflow)] [[demo](http://carpedm20.github.io/faces/)] [[related blogpost](https://swarbrickjones.wordpress.com/2016/01/24/generative-adversarial-autoencoders-in-theano/)]

* <NAME>, ["Understanding and Implementing Deepmind's DRAW Model"] (http://evjang.com/articles/draw)

* Denton, <NAME>., <NAME>, and <NAME>. ["Deep Generative Image Models using a￼ Laplacian Pyramid of Adversarial Networks."](http://papers.nips.cc/paper/5773-deep-generative-image-models-using-a-laplacian-pyramid-of-adversarial-networks) In Advances in neural information processing systems, pp. 1486-1494. 2015.

* <NAME>., <NAME>, <NAME>, <NAME>, <NAME>, and <NAME>. ["Attend, Infer, Repeat: Fast Scene Understanding with Generative Models."](http://arxiv.org/abs/1603.08575) 2016. [[video](https://youtu.be/4tc84kKdpY4)]

* Larsen, <NAME>, <NAME>, and <NAME>. ["Autoencoding beyond pixels using a learned similarity metric."](http://arxiv.org/abs/1512.09300) arXiv preprint arXiv:1512.09300 (2015).

* <NAME>, <NAME>, <NAME>, <NAME> and <NAME>. [Context Encoders: Feature Learning by Inpainting.](http://www.cs.berkeley.edu/~pathak/context_encoder/)  In CVPR 2016.

* Reed, Scott, <NAME>, <NAME>, <NAME>, <NAME>, and <NAME>. ["Generative adversarial text to image synthesis."](http://arxiv.org/abs/1605.05396) arXiv preprint arXiv:1605.05396 (2016).

* <NAME>, <NAME>, <NAME>, <NAME>, <NAME>, <NAME>, ["Conditional Image Generation with PixelCNN Decoders"](http://arxiv.org/abs/1606.05328), arXiv preprint arXiv:1606.05328 (2016).

* Russ	Salakhutdinov, [Learning	Deep	Generative Models](http://www.cs.toronto.edu/~rsalakhu/talk_Montreal_2016_Salakhutdinov.pdf). Carnegie Mellon University, 2016.
<commit_msg>NIPS 2016 Workshop on Adversarial Training<commit_after>---
layout: page
title: Adversarial Networks
permalink: /adversarial
hide: true
---

## Instructor

| ![<NAME>][KevinMcGuinness-photo]  |
|:-:|
|  [<NAME> (KM)](KevinMcGuinness-web)     |

[KevinMcGuinness-web]: https://www.insight-centre.org/users/kevin-mcguinness
[KevinMcGuinness-photo]: img/instructors/KevinMcGuinness.jpg "<NAME>"

* [Slides](slides/D4L1-adversarial.pdf)

## Videos

* Sirajology, ["Generative Adversarial Nets"](https://www.youtube.com/watch?v=deyOX6Mt_As). Fresh Machine Learning, July 2016. 

## Tutorials

* <NAME>, <NAME>, <NAME>, <NAME>, <NAME>, <NAME>, <NAME>, <NAME>, <NAME>, <NAME>, <NAME>, <NAME>, <NAME>, AND <NAME>AREMBA, ["Generative models"](https://openai.com/blog/generative-models/). OpenAI 2016.

* O'Shea Research, [MNIST Generative Adversarial Model in Keras](https://oshearesearch.com/index.php/2016/07/01/mnist-generative-adversarial-model-in-keras/). July 2016.

* <NAME>, ["Image Completion with Deep Learning in TensorFlow"](http://bamos.github.io/2016/08/09/deep-completion/). 2016.


## Related Work & Resources

* Szegedy, Christian, <NAME>, <NAME>, <NAME>, <NAME>, <NAME>, and <NAME>. ["Intriguing properties of neural networks."](http://arxiv.org/abs/1312.6199) arXiv preprint arXiv:1312.6199 (2013). [[UPC slides](https://docs.google.com/presentation/d/1g58oIdS_a9uEeiuDHC85OrU2tql3Prrn_jcHCPjB6pU/edit?usp=sharing)]

* [Practical Black-Box Attacks against Deep Learning Systems using Adversarial Examples](http://arxiv.org/abs/1602.02697)

* Im, <NAME>, <NAME> Kim, <NAME>, and <NAME>. ["Generating images with recurrent adversarial networks."](http://arxiv.org/abs/1602.05110) arXiv preprint arXiv:1602.05110 (2016). [[software](https://github.com/jiwoongim/GRAN)]

* Radford, Alec, <NAME>, and <NAME>. ["Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks."](http://arxiv.org/abs/1511.06434) arXiv preprint arXiv:1511.06434 (2015). [[Theano](https://github.com/Newmu/dcgan_code)] [[TensorFlow](https://github.com/jazzsaxmafia/dcgan_tensorflow)] [[reddit](https://www.reddit.com/r/MachineLearning/comments/3tykrw/unsupervised_representation_learning_with_deep/)]

* Dumoulin, Vincent, <NAME>, <NAME>, <NAME>, <NAME>, <NAME>, and <NAME>. ["Adversarially Learned Inference."](https://ishmaelbelghazi.github.io/ALI/) arXiv preprint arXiv:1606.00704 (2016).

* Donahue, Jeff, <NAME>, and <NAME>. ["Adversarial Feature Learning."](http://arxiv.org/abs/1605.09782) arXiv preprint arXiv:1605.09782 (2016).

* Goodfellow, Ian, <NAME>, <NAME>, <NAME>, <NAME>, <NAME>, <NAME>, and <NAME>. ["Generative adversarial nets."](http://papers.nips.cc/paper/5423-generative-adversarial) In Advances in Neural Information Processing Systems, pp. 2672-2680. 2014.

* Radford, Alec, <NAME>, and <NAME>. ["Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks."](http://arxiv.org/abs/1511.06434) International Conference on Learning Representations (2016). [[software](https://github.com/Newmu/dcgan_code)] [[software Theano](https://github.com/mikesj-public/dcgan-autoencoder)] [[software TensorFlow](https://github.com/carpedm20/DCGAN-tensorflow)] [[demo](http://carpedm20.github.io/faces/)] [[related blogpost](https://swarbrickjones.wordpress.com/2016/01/24/generative-adversarial-autoencoders-in-theano/)]

* <NAME>, ["Understanding and Implementing Deepmind's DRAW Model"] (http://evjang.com/articles/draw)

* Denton, <NAME>., <NAME>, and <NAME>. ["Deep Generative Image Models using a￼ Laplacian Pyramid of Adversarial Networks."](http://papers.nips.cc/paper/5773-deep-generative-image-models-using-a-laplacian-pyramid-of-adversarial-networks) In Advances in neural information processing systems, pp. 1486-1494. 2015.

* <NAME>., <NAME>, <NAME>, <NAME>, <NAME>, and <NAME>. ["Attend, Infer, Repeat: Fast Scene Understanding with Generative Models."](http://arxiv.org/abs/1603.08575) 2016. [[video](https://youtu.be/4tc84kKdpY4)]

* Larsen, <NAME>, <NAME>, and <NAME>. ["Autoencoding beyond pixels using a learned similarity metric."](http://arxiv.org/abs/1512.09300) arXiv preprint arXiv:1512.09300 (2015).

* <NAME>, <NAME>, <NAME>, <NAME> and <NAME>. [Context Encoders: Feature Learning by Inpainting.](http://www.cs.berkeley.edu/~pathak/context_encoder/)  In CVPR 2016.

* Reed, Scott, <NAME>, <NAME>, <NAME>, <NAME>, and <NAME>. ["Generative adversarial text to image synthesis."](http://arxiv.org/abs/1605.05396) arXiv preprint arXiv:1605.05396 (2016).

* <NAME>, <NAME>, <NAME>, <NAME>, <NAME>, Koray Kavukcuoglu, ["Conditional Image Generation with PixelCNN Decoders"](http://arxiv.org/abs/1606.05328), arXiv preprint arXiv:1606.05328 (2016).

* <NAME>, [Learning	Deep	Generative Models](http://www.cs.toronto.edu/~rsalakhu/talk_Montreal_2016_Salakhutdinov.pdf). Carnegie Mellon University, 2016.

* [NIPS 2016 Workshop on Adversarial Training](https://sites.google.com/site/nips2016adversarial/)

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