add iGAN

README.md

@@ -334,8 +334,9 @@ Please feel free to [pull requests](https://github.com/kjw0612/awesome-deep-visi
   * Jost Tobias Springenberg, "Unsupervised and Semi-supervised Learning with Categorical Generative Adversarial Networks", ICLR 2016, [[Paper](http://arxiv.org/pdf/1511.06390v1.pdf)]
   * Harrison Edwards, Amos Storkey, "Censoring Representations with an Adversary", ICLR 2016, [[Paper](http://arxiv.org/pdf/1511.05897v3.pdf)]
   * Takeru Miyato, Shin-ichi Maeda, Masanori Koyama, Ken Nakae, Shin Ishii, "Distributional Smoothing with Virtual Adversarial Training", ICLR 2016, [[Paper](http://arxiv.org/pdf/1507.00677v8.pdf)]
+  * Jun-Yan Zhu, Philipp Krahenbuhl, Eli Shechtman, and Alexei A. Efros, "Generative Visual Manipulation on the Natural Image Manifold", ECCV 2016. [[Paper](https://arxiv.org/pdf/1609.03552v2.pdf)] [[Code](https://github.com/junyanz/iGAN)] [[Video](https://youtu.be/9c4z6YsBGQ0)]
 * Mixing Convolutional and Adversarial Networks
-  * Alec Radford, Luke Metz, Soumith Chintala, "Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks", ICLR 2016. [[Paper](http://arxiv.org/pdf/1511.06434v2.pdf)]
+  * Alec Radford, Luke Metz, Soumith Chintala, "Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks", ICLR 2016. [[Paper](http://arxiv.org/pdf/1511.06434.pdf)]
 
 ### Other Topics
 * Visual Analogy [[Paper](https://web.eecs.umich.edu/~honglak/nips2015-analogy.pdf)]