Moving "Image Generation" papers to a new topic

Moving the subtopic "Image Generation" from "Other Topics" to a new topic

README.md

@@ -303,6 +303,22 @@ Please feel free to [pull requests](https://github.com/kjw0612/awesome-deep-visi
 * MetaMind [[Paper](http://arxiv.org/pdf/1603.01417v1.pdf)]
   * Xiong, Caiming, Stephen Merity, and Richard Socher. "Dynamic Memory Networks for Visual and Textual Question Answering." arXiv:1603.01417 (2016).
 
+### Image Generation
+* Convolutional / Recurrent Networks
+  * Alexey Dosovitskiy, Jost Tobias Springenberg, Thomas Brox, "Learning to Generate Chairs with Convolutional Neural Networks", CVPR, 2015. [[Paper]](http://www.cv-foundation.org/openaccess/content_cvpr_2015/papers/Dosovitskiy_Learning_to_Generate_2015_CVPR_paper.pdf)
+  * Karol Gregor, Ivo Danihelka, Alex Graves, Danilo Jimenez Rezende, Daan Wierstra, "DRAW: A Recurrent Neural Network For Image Generation", ICML, 2015. [[Paper](https://arxiv.org/pdf/1502.04623v2.pdf)] 
+* Adversarial Networks
+  * Ian J. Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron Courville, Yoshua Bengio, Generative Adversarial Networks, NIPS, 2014. [[Paper]](http://arxiv.org/abs/1406.2661)
+  * Emily Denton, Soumith Chintala, Arthur Szlam, Rob Fergus, Deep Generative Image Models using a Laplacian Pyramid of Adversarial Networks, NIPS, 2015. [[Paper]](http://arxiv.org/abs/1506.05751)
+  * Lucas Theis, Aäron van den Oord, Matthias Bethge, "A note on the evaluation of generative models", ICLR 2016. [[Paper](http://arxiv.org/abs/1511.01844)]
+  * Zhenwen Dai, Andreas Damianou, Javier Gonzalez, Neil Lawrence, "Variationally Auto-Encoded Deep Gaussian Processes", ICLR 2016. [[Paper](http://arxiv.org/pdf/1511.06455v2.pdf)]
+  * Elman Mansimov, Emilio Parisotto, Jimmy Ba, Ruslan Salakhutdinov, "Generating Images from Captions with Attention", ICLR 2016, [[Paper](http://arxiv.org/pdf/1511.02793v2.pdf)]
+  * 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)]
+* 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/abs/1511.06434)]
+
 ### Other Topics
 * Visual Analogy [[Paper](https://web.eecs.umich.edu/~honglak/nips2015-analogy.pdf)]
   * Scott Reed, Yi Zhang, Yuting Zhang, Honglak Lee, Deep Visual Analogy Making, NIPS, 2015
@@ -314,21 +330,8 @@ Please feel free to [pull requests](https://github.com/kjw0612/awesome-deep-visi
   * Cong Zhang, Hongsheng Li, Xiaogang Wang, Xiaokang Yang, Cross-scene Crowd Counting via Deep Convolutional Neural Networks, CVPR, 2015.
 * 3D Shape Retrieval [[Paper]](http://www.cv-foundation.org/openaccess/content_cvpr_2015/papers/Wang_Sketch-Based_3D_Shape_2015_CVPR_paper.pdf)
   * Fang Wang, Le Kang, Yi Li, Sketch-based 3D Shape Retrieval using Convolutional Neural Networks, CVPR, 2015.
-* Image Generation 
-  * Alexey Dosovitskiy, Jost Tobias Springenberg, Thomas Brox, "Learning to Generate Chairs with Convolutional Neural Networks", CVPR, 2015. [[Paper]](http://www.cv-foundation.org/openaccess/content_cvpr_2015/papers/Dosovitskiy_Learning_to_Generate_2015_CVPR_paper.pdf)
-  * Karol Gregor, Ivo Danihelka, Alex Graves, Danilo Jimenez Rezende, Daan Wierstra, "DRAW: A Recurrent Neural Network For Image Generation", ICML, 2015. [[Paper](https://arxiv.org/pdf/1502.04623v2.pdf)] 
 * Weakly-supervised Classification
   * Samaneh Azadi, Jiashi Feng, Stefanie Jegelka, Trevor Darrell, "Auxiliary Image Regularization for Deep CNNs with Noisy Labels", ICLR 2016, [[Paper](http://arxiv.org/pdf/1511.07069v2.pdf)]
-* Image Generation with Adversarial Network
-  * Ian J. Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron Courville, Yoshua Bengio, Generative Adversarial Networks, NIPS, 2014. [[Paper]](http://arxiv.org/abs/1406.2661)
-  * Emily Denton, Soumith Chintala, Arthur Szlam, Rob Fergus, Deep Generative Image Models using a Laplacian Pyramid of Adversarial Networks, NIPS, 2015. [[Paper]](http://arxiv.org/abs/1506.05751)
-  * Alec Radford, Luke Metz, Soumith Chintala, "Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks", ICLR 2016. [[Paper](http://arxiv.org/abs/1511.06434)]
-  * Lucas Theis, Aäron van den Oord, Matthias Bethge, "A note on the evaluation of generative models", ICLR 2016. [[Paper](http://arxiv.org/abs/1511.01844)]
-  * Zhenwen Dai, Andreas Damianou, Javier Gonzalez, Neil Lawrence, "Variationally Auto-Encoded Deep Gaussian Processes", ICLR 2016. [[Paper](http://arxiv.org/pdf/1511.06455v2.pdf)]
-  * Elman Mansimov, Emilio Parisotto, Jimmy Ba, Ruslan Salakhutdinov, "Generating Images from Captions with Attention", ICLR 2016, [[Paper](http://arxiv.org/pdf/1511.02793v2.pdf)]
-  * 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)]
 * Artistic Style [[Paper]](http://arxiv.org/abs/1508.06576) [[Code]](https://github.com/jcjohnson/neural-style)
   * Leon A. Gatys, Alexander S. Ecker, Matthias Bethge, A Neural Algorithm of Artistic Style.
 * Human Gaze Estimation