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Jonas Zeunert
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 Awesome Deep Vision !Awesome (https://cdn.rawgit.com/sindresorhus/awesome/d7305f38d29fed78fa85652e3a63e154dd8e8829/media/badge.svg) (https://github.com/sindresorhus/awesome)
 Awesome Deep Vision !Awesome (https://cdn.rawgit.com/sindresorhus/awesome/d7305f38d29fed78fa85652e3a63e154dd8e8829/media/badge.svg) (https://github.com/sindresorhus/awesome)
A curated list of deep learning resources for computer vision, inspired by awesome-php (https://github.com/ziadoz/awesome-php) and awesome-computer-vision (https://github.com/jbhuang0604/awesome-computer-vision).
@@ -135,7 +135,7 @@
  ⟡ Justin Johnson, Alexandre Alahi, Li Fei-Fei, Perceptual Losses for Real-Time Style Transfer and Super-Resolution, arXiv:1603.08155, 2016. Paper  (http://arxiv.org/abs/1603.08155) Supplementary  
(http://cs.stanford.edu/people/jcjohns/papers/fast-style/fast-style-supp.pdf)
⟡ SRGAN
  ⟡ Christian Ledig, Lucas Theis, Ferenc Huszar, Jose Caballero, Andrew Cunningham, Alejandro Acosta, Andrew Aitken, Alykhan Tejani, Johannes Totz, Zehan Wang, Wenzhe Shi, Photo-Realistic Single Image Super-Resolution Using a Generative
  ⟡ Christian Ledig, Lucas Theis, Ferenc Huszar, Jose Caballero, Andrew Cunningham, Alejandro Acosta, Andrew Aitken, Alykhan Tejani, Johannes Totz, Zehan Wang, Wenzhe Shi, Photo-Realistic Single Image Super-Resolution Using a Generative 
Adversarial Network, arXiv:1609.04802v3, 2016. Paper  (https://arxiv.org/pdf/1609.04802v3.pdf)
⟡ Others
  ⟡ Osendorfer, Christian, Hubert Soyer, and Patrick van der Smagt, Image Super-Resolution with Fast Approximate Convolutional Sparse Coding, ICONIP, 2014. Paper ICONIP-2014  (http://brml.org/uploads/tx_sibibtex/281.pdf)
@@ -201,14 +201,12 @@
  ⟡ Mohammadreza Mostajabi, Payman Yadollahpour, Gregory Shakhnarovich, Feedforward Semantic Segmentation With Zoom-Out Features, CVPR, 2015
⟡ Joint Calibration Paper  (http://arxiv.org/pdf/1507.01581)
  ⟡ Holger Caesar, Jasper Uijlings, Vittorio Ferrari, Joint Calibration for Semantic Segmentation, arXiv:1507.01581.
⟡ Fully Convolutional Networks for Semantic Segmentation Paper-CVPR15  (http://www.cv-foundation.org/openaccess/content_cvpr_2015/papers/Long_Fully_Convolutional_Networks_2015_CVPR_paper.pdf) Paper-arXiv15  
(http://arxiv.org/pdf/1411.4038)
⟡ Fully Convolutional Networks for Semantic Segmentation Paper-CVPR15  (http://www.cv-foundation.org/openaccess/content_cvpr_2015/papers/Long_Fully_Convolutional_Networks_2015_CVPR_paper.pdf) Paper-arXiv15  (http://arxiv.org/pdf/1411.4038)
  ⟡ Jonathan Long, Evan Shelhamer, Trevor Darrell, Fully Convolutional Networks for Semantic Segmentation, CVPR, 2015.
⟡ Hypercolumn Paper  (http://www.cv-foundation.org/openaccess/content_cvpr_2015/papers/Hariharan_Hypercolumns_for_Object_2015_CVPR_paper.pdf)
  ⟡ Bharath Hariharan, Pablo Arbelaez, Ross Girshick, Jitendra Malik, Hypercolumns for Object Segmentation and Fine-Grained Localization, CVPR, 2015.
⟡ Deep Hierarchical Parsing
  ⟡ Abhishek Sharma, Oncel Tuzel, David W. Jacobs, Deep Hierarchical Parsing for Semantic Segmentation, CVPR, 2015. Paper  
(http://www.cv-foundation.org/openaccess/content_cvpr_2015/papers/Sharma_Deep_Hierarchical_Parsing_2015_CVPR_paper.pdf)
  ⟡ Abhishek Sharma, Oncel Tuzel, David W. Jacobs, Deep Hierarchical Parsing for Semantic Segmentation, CVPR, 2015. Paper  (http://www.cv-foundation.org/openaccess/content_cvpr_2015/papers/Sharma_Deep_Hierarchical_Parsing_2015_CVPR_paper.pdf)
⟡ Learning Hierarchical Features for Scene Labeling Paper-ICML12  (http://yann.lecun.com/exdb/publis/pdf/farabet-icml-12.pdf) Paper-PAMI13  (http://yann.lecun.com/exdb/publis/pdf/farabet-pami-13.pdf)
  ⟡ Clement Farabet, Camille Couprie, Laurent Najman, Yann LeCun, Scene Parsing with Multiscale Feature Learning, Purity Trees, and Optimal Covers, ICML, 2012.
  ⟡ Clement Farabet, Camille Couprie, Laurent Najman, Yann LeCun, Learning Hierarchical Features for Scene Labeling, PAMI, 2013.
@@ -262,8 +260,7 @@
(http://www.cv-foundation.org/openaccess/content_cvpr_2015/papers/Lenc_Understanding_Image_Representations_2015_CVPR_paper.pdf)
⟡ Anh Nguyen, Jason Yosinski, Jeff Clune, Deep Neural Networks are Easily Fooled:High Confidence Predictions for Unrecognizable Images, CVPR, 2015. Paper  
(http://www.cv-foundation.org/openaccess/content_cvpr_2015/papers/Nguyen_Deep_Neural_Networks_2015_CVPR_paper.pdf)
⟡ Aravindh Mahendran, Andrea Vedaldi, Understanding Deep Image Representations by Inverting Them, CVPR, 2015. Paper  
(http://www.cv-foundation.org/openaccess/content_cvpr_2015/papers/Mahendran_Understanding_Deep_Image_2015_CVPR_paper.pdf)
⟡ Aravindh Mahendran, Andrea Vedaldi, Understanding Deep Image Representations by Inverting Them, CVPR, 2015. Paper  (http://www.cv-foundation.org/openaccess/content_cvpr_2015/papers/Mahendran_Understanding_Deep_Image_2015_CVPR_paper.pdf)
⟡ Bolei Zhou, Aditya Khosla, Agata Lapedriza, Aude Oliva, Antonio Torralba, Object Detectors Emerge in Deep Scene CNNs, ICLR, 2015. arXiv Paper  (http://arxiv.org/abs/1412.6856)
⟡ Alexey Dosovitskiy, Thomas Brox, Inverting Visual Representations with Convolutional Networks, arXiv, 2015. Paper  (http://arxiv.org/abs/1506.02753)
⟡ Matthrew Zeiler, Rob Fergus, Visualizing and Understanding Convolutional Networks, ECCV, 2014. Paper  (https://www.cs.nyu.edu/~fergus/papers/zeilerECCV2014.pdf)
@@ -291,8 +288,7 @@
  ⟡ Xinlei Chen, C. Lawrence Zitnick, Learning a Recurrent Visual Representation for Image Caption Generation, arXiv:1411.5654.
  ⟡ Xinlei Chen, C. Lawrence Zitnick, Mind’s Eye: A Recurrent Visual Representation for Image Caption Generation, CVPR 2015
⟡ Microsoft Paper  (http://arxiv.org/pdf/1411.4952)
  ⟡ Hao Fang, Saurabh Gupta, Forrest Iandola, Rupesh Srivastava, Li Deng, Piotr Dollár, Jianfeng Gao, Xiaodong He, Margaret Mitchell, John C. Platt, C. Lawrence Zitnick, Geoffrey Zweig, From Captions to Visual Concepts and Back, CVPR, 
2015.
  ⟡ Hao Fang, Saurabh Gupta, Forrest Iandola, Rupesh Srivastava, Li Deng, Piotr Dollár, Jianfeng Gao, Xiaodong He, Margaret Mitchell, John C. Platt, C. Lawrence Zitnick, Geoffrey Zweig, From Captions to Visual Concepts and Back, CVPR, 2015.
⟡ Univ. Montreal / Univ. Toronto Web (http://kelvinxu.github.io/projects/capgen.html) Paper (http://www.cs.toronto.edu/~zemel/documents/captionAttn.pdf) 
  ⟡ Kelvin Xu, Jimmy Lei Ba, Ryan Kiros, Kyunghyun Cho, Aaron Courville, Ruslan Salakhutdinov, Richard S. Zemel, Yoshua Bengio, Show, Attend, and Tell: Neural Image Caption Generation with Visual Attention, arXiv:1502.03044 / ICML 2015
⟡ Idiap / EPFL / Facebook Paper (http://arxiv.org/pdf/1502.03671) 
@@ -301,7 +297,7 @@
  ⟡ Junhua Mao, Wei Xu, Yi Yang, Jiang Wang, Zhiheng Huang, Alan L. Yuille, Learning like a Child: Fast Novel Visual Concept Learning from Sentence Descriptions of Images, arXiv:1504.06692
⟡ MS + Berkeley
  ⟡ Jacob Devlin, Saurabh Gupta, Ross Girshick, Margaret Mitchell, C. Lawrence Zitnick, Exploring Nearest Neighbor Approaches for Image Captioning, arXiv:1505.04467 Paper (http://arxiv.org/pdf/1505.04467.pdf) 
  ⟡ Jacob Devlin, Hao Cheng, Hao Fang, Saurabh Gupta, Li Deng, Xiaodong He, Geoffrey Zweig, Margaret Mitchell, Language Models for Image Captioning: The Quirks and What Works, arXiv:1505.01809 Paper (http://arxiv.org/pdf/1505.01809.pdf)
  ⟡ Jacob Devlin, Hao Cheng, Hao Fang, Saurabh Gupta, Li Deng, Xiaodong He, Geoffrey Zweig, Margaret Mitchell, Language Models for Image Captioning: The Quirks and What Works, arXiv:1505.01809 Paper (http://arxiv.org/pdf/1505.01809.pdf) 
⟡ Adelaide Paper (http://arxiv.org/pdf/1506.01144.pdf) 
  ⟡ Qi Wu, Chunhua Shen, Anton van den Hengel, Lingqiao Liu, Anthony Dick, Image Captioning with an Intermediate Attributes Layer, arXiv:1506.01144
⟡ Tilburg Paper (http://arxiv.org/pdf/1506.03694.pdf) 
@@ -377,8 +373,8 @@
  ⟡ 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) 
  ⟡ 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/1511.06434.pdf) 
@@ -468,3 +464,5 @@
⟡ Facebook's AI Painting@Wired (http://www.wired.com/2015/06/facebook-googles-fake-brains-spawn-new-visual-reality/)
⟡ Inceptionism: Going Deeper into Neural Networks@Google Research (http://googleresearch.blogspot.kr/2015/06/inceptionism-going-deeper-into-neural.html)
⟡ Implementing Neural networks (http://peterroelants.github.io/) 
deepvision Github: https://github.com/kjw0612/awesome-deep-vision