fix spacings/alignments

README.md

@@ -67,26 +67,26 @@ Please feel free to [pull requests](https://github.com/kjw0612/awesome-deep-visi
 (from Shaoqing Ren, Kaiming He, Ross Girshick, Jian Sun, Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks, arXiv:1506.01497.)
 
 * OverFeat, NYU [[Paper]](http://arxiv.org/pdf/1312.6229.pdf)
-* OverFeat: Integrated Recognition, Localization and Detection using Convolutional Networks, ICLR, 2014.
+  * OverFeat: Integrated Recognition, Localization and Detection using Convolutional Networks, ICLR, 2014.
 * R-CNN, UC Berkeley [[Paper-CVPR14]](http://www.cv-foundation.org/openaccess/content_cvpr_2014/papers/Girshick_Rich_Feature_Hierarchies_2014_CVPR_paper.pdf) [[Paper-arXiv14]](http://arxiv.org/pdf/1311.2524)
-* Ross Girshick, Jeff Donahue, Trevor Darrell, Jitendra Malik, Rich feature hierarchies for accurate object detection and semantic segmentation, CVPR, 2014.
+  * Ross Girshick, Jeff Donahue, Trevor Darrell, Jitendra Malik, Rich feature hierarchies for accurate object detection and semantic segmentation, CVPR, 2014.
 * SPP, Microsoft Research [[Paper]](http://arxiv.org/pdf/1406.4729)
-* Kaiming He, Xiangyu Zhang, Shaoqing Ren, Jian Sun, Spatial Pyramid Pooling in Deep Convolutional Networks for Visual Recognition, ECCV, 2014.
+  * Kaiming He, Xiangyu Zhang, Shaoqing Ren, Jian Sun, Spatial Pyramid Pooling in Deep Convolutional Networks for Visual Recognition, ECCV, 2014.
 * Fast R-CNN, Microsoft Research [[Paper]] (http://arxiv.org/pdf/1504.08083)
-* Ross Girshick, Fast R-CNN, arXiv:1504.08083.
+  * Ross Girshick, Fast R-CNN, arXiv:1504.08083.
 * Faster R-CNN, Microsoft Research [[Paper]] (http://arxiv.org/pdf/1506.01497)
-* Shaoqing Ren, Kaiming He, Ross Girshick, Jian Sun, Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks, arXiv:1506.01497.
+  * Shaoqing Ren, Kaiming He, Ross Girshick, Jian Sun, Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks, arXiv:1506.01497.
 * R-CNN minus R, Oxford [[Paper]] (http://arxiv.org/pdf/1506.06981)
-* Karel Lenc, Andrea Vedaldi, R-CNN minus R, arXiv:1506.06981.
+  * Karel Lenc, Andrea Vedaldi, R-CNN minus R, arXiv:1506.06981.
 * End-to-end people detection in crowded scenes [[Paper]] (http://arxiv.org/abs/1506.04878)
-* Russell Stewart, Mykhaylo Andriluka, End-to-end people detection in crowded scenes, arXiv:1506.04878.
+  * Russell Stewart, Mykhaylo Andriluka, End-to-end people detection in crowded scenes, arXiv:1506.04878.
 * You Only Look Once: Unified, Real-Time Object Detection [[Paper]] (http://arxiv.org/abs/1506.02640)
-* Joseph Redmon, Santosh Divvala, Ross Girshick, Ali Farhadi, You Only Look Once: Unified, Real-Time Object Detection, arXiv:1506.02640
+  * Joseph Redmon, Santosh Divvala, Ross Girshick, Ali Farhadi, You Only Look Once: Unified, Real-Time Object Detection, arXiv:1506.02640
 * Inside-Outside Net [[Paper]](http://arxiv.org/abs/1512.04143)
-* Sean Bell, C. Lawrence Zitnick, Kavita Bala, Ross Girshick, Inside-Outside Net: Detecting Objects in Context with Skip Pooling and Recurrent Neural Networks
+  * Sean Bell, C. Lawrence Zitnick, Kavita Bala, Ross Girshick, Inside-Outside Net: Detecting Objects in Context with Skip Pooling and Recurrent Neural Networks
 * Deep Residual Network (Current State-of-the-Art) [[Paper]](http://arxiv.org/abs/1512.03385)
-* Kaiming He, Xiangyu Zhang, Shaoqing Ren, Jian Sun, Deep Residual Learning for Image Recognition
-* Weakly Supervised Object Localization with Multi-fold Multiple Instance Learning [[Paper]] (http://arxiv.org/pdf/1503.00949.pdf)
+  * Kaiming He, Xiangyu Zhang, Shaoqing Ren, Jian Sun, Deep Residual Learning for Image Recognition
+* Weakly Supervised Object Localization with Multi-fold Multiple Instance Learning [[Paper](http://arxiv.org/pdf/1503.00949.pdf)]
 
 ### Video Classification
 * Nicolas Ballas, Li Yao, Pal Chris, Aaron Courville, "Delving Deeper into Convolutional Networks for Learning Video Representations", ICLR 2016. [[Paper](http://arxiv.org/pdf/1511.06432v4.pdf)]
@@ -96,9 +96,9 @@ Please feel free to [pull requests](https://github.com/kjw0612/awesome-deep-visi
 * Seunghoon Hong, Tackgeun You, Suha Kwak, Bohyung Han, Online Tracking by Learning Discriminative Saliency Map with Convolutional Neural Network, arXiv:1502.06796. [[Paper]](http://arxiv.org/pdf/1502.06796)
 * Hanxi Li, Yi Li and Fatih Porikli, DeepTrack: Learning Discriminative Feature Representations by Convolutional Neural Networks for Visual Tracking, BMVC, 2014. [[Paper]](http://www.bmva.org/bmvc/2014/files/paper028.pdf)
 * N Wang, DY Yeung, Learning a Deep Compact Image Representation for Visual Tracking, NIPS, 2013. [[Paper]](http://winsty.net/papers/dlt.pdf)
-* Chao Ma, Jia-Bin Huang, Xiaokang Yang and Ming-Hsuan Yang, Hierarchical Convolutional Features for Visual Tracking, ICCV 2015 [[GitHub]](https://github.com/jbhuang0604/CF2)
-* Lijun Wang, Wanli Ouyang, Xiaogang Wang, and Huchuan Lu, Visual Tracking with fully Convolutional Networks, ICCV 2015 [[GitHub]](https://github.com/scott89/FCNT) [[Paper]](http://202.118.75.4/lu/Paper/ICCV2015/iccv15_lijun.pdf)
-  * Hyeonseob Namand Bohyung Han, Learning Multi-Domain Convolutional Neural Networks for Visual Tracking, [[Paper](http://arxiv.org/pdf/1510.07945.pdf)] [[Code](https://github.com/HyeonseobNam/MDNet)] [[Project Page](http://cvlab.postech.ac.kr/research/mdnet/)]
+* Chao Ma, Jia-Bin Huang, Xiaokang Yang and Ming-Hsuan Yang, Hierarchical Convolutional Features for Visual Tracking, ICCV 2015 [[Paper](http://www.cv-foundation.org/openaccess/content_iccv_2015/papers/Ma_Hierarchical_Convolutional_Features_ICCV_2015_paper.pdf)] [[Code](https://github.com/jbhuang0604/CF2)]
+* Lijun Wang, Wanli Ouyang, Xiaogang Wang, and Huchuan Lu, Visual Tracking with fully Convolutional Networks, ICCV 2015  [[Paper](http://202.118.75.4/lu/Paper/ICCV2015/iccv15_lijun.pdf)] [[Code](https://github.com/scott89/FCNT)]
+* Hyeonseob Namand Bohyung Han, Learning Multi-Domain Convolutional Neural Networks for Visual Tracking, [[Paper](http://arxiv.org/pdf/1510.07945.pdf)] [[Code](https://github.com/HyeonseobNam/MDNet)] [[Project Page](http://cvlab.postech.ac.kr/research/mdnet/)]
 
 ### Low-Level Vision
 
@@ -107,41 +107,41 @@ Please feel free to [pull requests](https://github.com/kjw0612/awesome-deep-visi
   * Chao Dong, Chen Change Loy, Kaiming He, Xiaoou Tang, Learning a Deep Convolutional Network for Image Super-Resolution, ECCV, 2014.
   * Chao Dong, Chen Change Loy, Kaiming He, Xiaoou Tang, Image Super-Resolution Using Deep Convolutional Networks, arXiv:1501.00092.
 * Very Deep Super-Resolution
-* Jiwon Kim, Jung Kwon Lee, Kyoung Mu Lee, Accurate Image Super-Resolution Using Very Deep Convolutional Networks, arXiv:1511.04587, 2015. [[Paper]](http://arxiv.org/abs/1511.04587)
+  * Jiwon Kim, Jung Kwon Lee, Kyoung Mu Lee, Accurate Image Super-Resolution Using Very Deep Convolutional Networks, arXiv:1511.04587, 2015. [[Paper]](http://arxiv.org/abs/1511.04587)
 * Deeply-Recursive Convolutional Network
-* Jiwon Kim, Jung Kwon Lee, Kyoung Mu Lee, Deeply-Recursive Convolutional Network for Image Super-Resolution, arXiv:1511.04491, 2015. [[Paper]](http://arxiv.org/abs/1511.04491)
+  * Jiwon Kim, Jung Kwon Lee, Kyoung Mu Lee, Deeply-Recursive Convolutional Network for Image Super-Resolution, arXiv:1511.04491, 2015. [[Paper]](http://arxiv.org/abs/1511.04491)
 * Casade-Sparse-Coding-Network
-* Zhaowen Wang, Ding Liu, Wei Han, Jianchao Yang and Thomas S. Huang, Deep Networks for Image Super-Resolution with Sparse Prior. ICCV, 2015. [[Paper]](http://www.ifp.illinois.edu/~dingliu2/iccv15/iccv15.pdf) [[Code]](http://www.ifp.illinois.edu/~dingliu2/iccv15/)
+  * Zhaowen Wang, Ding Liu, Wei Han, Jianchao Yang and Thomas S. Huang, Deep Networks for Image Super-Resolution with Sparse Prior. ICCV, 2015. [[Paper]](http://www.ifp.illinois.edu/~dingliu2/iccv15/iccv15.pdf) [[Code]](http://www.ifp.illinois.edu/~dingliu2/iccv15/)
 * Perceptual Losses for Super-Resolution
-* 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)
+  * 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)
 * 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)
 
 #### Other Applications
 * Optical Flow (FlowNet) [[Paper]](http://arxiv.org/pdf/1504.06852)
-* Philipp Fischer, Alexey Dosovitskiy, Eddy Ilg, Philip Häusser, Caner Hazırbaş, Vladimir Golkov, Patrick van der Smagt, Daniel Cremers, Thomas Brox, FlowNet: Learning Optical Flow with Convolutional Networks, arXiv:1504.06852.
+  * Philipp Fischer, Alexey Dosovitskiy, Eddy Ilg, Philip Häusser, Caner Hazırbaş, Vladimir Golkov, Patrick van der Smagt, Daniel Cremers, Thomas Brox, FlowNet: Learning Optical Flow with Convolutional Networks, arXiv:1504.06852.
 * Compression Artifacts Reduction [[Paper-arXiv15]](http://arxiv.org/pdf/1504.06993)
   * Chao Dong, Yubin Deng, Chen Change Loy, Xiaoou Tang, Compression Artifacts Reduction by a Deep Convolutional Network, arXiv:1504.06993.
 * Blur Removal
-* Christian J. Schuler, Michael Hirsch, Stefan Harmeling, Bernhard Schölkopf, Learning to Deblur, arXiv:1406.7444 [[Paper]](http://arxiv.org/pdf/1406.7444.pdf)
-* Jian Sun, Wenfei Cao, Zongben Xu, Jean Ponce, Learning a Convolutional Neural Network for Non-uniform Motion Blur Removal, CVPR, 2015 [[Paper]](http://arxiv.org/pdf/1503.00593)
+  * Christian J. Schuler, Michael Hirsch, Stefan Harmeling, Bernhard Schölkopf, Learning to Deblur, arXiv:1406.7444 [[Paper]](http://arxiv.org/pdf/1406.7444.pdf)
+  * Jian Sun, Wenfei Cao, Zongben Xu, Jean Ponce, Learning a Convolutional Neural Network for Non-uniform Motion Blur Removal, CVPR, 2015 [[Paper]](http://arxiv.org/pdf/1503.00593)
 * Image Deconvolution [[Web]](http://lxu.me/projects/dcnn/) [[Paper]](http://lxu.me/mypapers/dcnn_nips14.pdf)
-* Li Xu, Jimmy SJ. Ren, Ce Liu, Jiaya Jia, Deep Convolutional Neural Network for Image Deconvolution, NIPS, 2014.
+  * Li Xu, Jimmy SJ. Ren, Ce Liu, Jiaya Jia, Deep Convolutional Neural Network for Image Deconvolution, NIPS, 2014.
 * Deep Edge-Aware Filter [[Paper]](http://jmlr.org/proceedings/papers/v37/xub15.pdf)
-* Li Xu, Jimmy SJ. Ren, Qiong Yan, Renjie Liao, Jiaya Jia, Deep Edge-Aware Filters, ICML, 2015.
+  * Li Xu, Jimmy SJ. Ren, Qiong Yan, Renjie Liao, Jiaya Jia, Deep Edge-Aware Filters, ICML, 2015.
 * Computing the Stereo Matching Cost with a Convolutional Neural Network [[Paper]](http://www.cv-foundation.org/openaccess/content_cvpr_2015/papers/Zbontar_Computing_the_Stereo_2015_CVPR_paper.pdf)
-* Jure Žbontar, Yann LeCun, Computing the Stereo Matching Cost with a Convolutional Neural Network, CVPR, 2015.
+  * Jure Žbontar, Yann LeCun, Computing the Stereo Matching Cost with a Convolutional Neural Network, CVPR, 2015.
 
 ### Edge Detection
 ![edge_detection](https://cloud.githubusercontent.com/assets/5226447/8452371/93ca6f7e-2025-11e5-90f2-d428fd5ff7ac.PNG)
 (from Gedas Bertasius, Jianbo Shi, Lorenzo Torresani, DeepEdge: A Multi-Scale Bifurcated Deep Network for Top-Down Contour Detection, CVPR, 2015.)
 
 * Holistically-Nested Edge Detection [[Paper]](http://arxiv.org/pdf/1504.06375) [[Code]](https://github.com/s9xie/hed)
-* Saining Xie, Zhuowen Tu, Holistically-Nested Edge Detection, arXiv:1504.06375.
+  * Saining Xie, Zhuowen Tu, Holistically-Nested Edge Detection, arXiv:1504.06375.
 * DeepEdge [[Paper]](http://arxiv.org/pdf/1412.1123)
-* Gedas Bertasius, Jianbo Shi, Lorenzo Torresani, DeepEdge: A Multi-Scale Bifurcated Deep Network for Top-Down Contour Detection, CVPR, 2015.
+  * Gedas Bertasius, Jianbo Shi, Lorenzo Torresani, DeepEdge: A Multi-Scale Bifurcated Deep Network for Top-Down Contour Detection, CVPR, 2015.
 * DeepContour [[Paper]](http://mc.eistar.net/UpLoadFiles/Papers/DeepContour_cvpr15.pdf)
-* Wei Shen, Xinggang Wang, Yan Wang, Xiang Bai, Zhijiang Zhang, DeepContour: A Deep Convolutional Feature Learned by Positive-Sharing Loss for Contour Detection, CVPR, 2015.
+  * Wei Shen, Xinggang Wang, Yan Wang, Xiang Bai, Zhijiang Zhang, DeepContour: A Deep Convolutional Feature Learned by Positive-Sharing Loss for Contour Detection, CVPR, 2015.
 
 ### Semantic Segmentation
 ![semantic_segmantation](https://cloud.githubusercontent.com/assets/5226447/8452076/0ba8340c-2023-11e5-88bc-bebf4509b6bb.PNG)
@@ -150,74 +150,72 @@ Please feel free to [pull requests](https://github.com/kjw0612/awesome-deep-visi
   ![VOC2012_top_rankings](https://cloud.githubusercontent.com/assets/7778428/11551711/23ab9b46-99bf-11e5-85f4-35b27c5d6eaf.png)
   (from PASCAL VOC2012 [leaderboards](http://host.robots.ox.ac.uk:8080/leaderboard/displaylb.php?challengeid=11&compid=6))
 * Adelaide
-* Guosheng Lin, Chunhua Shen, Ian Reid, Anton van dan Hengel, Efficient piecewise training of deep structured models for semantic segmentation, arXiv:1504.01013. [[Paper]](http://arxiv.org/pdf/1504.01013) (1st ranked in VOC2012)
-* Guosheng Lin, Chunhua Shen, Ian Reid, Anton van den Hengel, Deeply Learning the Messages in Message Passing Inference, arXiv:1508.02108. [[Paper]](http://arxiv.org/pdf/1506.02108) (4th ranked in VOC2012)
+  * Guosheng Lin, Chunhua Shen, Ian Reid, Anton van dan Hengel, Efficient piecewise training of deep structured models for semantic segmentation, arXiv:1504.01013. [[Paper]](http://arxiv.org/pdf/1504.01013) (1st ranked in VOC2012)
+  * Guosheng Lin, Chunhua Shen, Ian Reid, Anton van den Hengel, Deeply Learning the Messages in Message Passing Inference, arXiv:1508.02108. [[Paper]](http://arxiv.org/pdf/1506.02108) (4th ranked in VOC2012)
 * Deep Parsing Network (DPN)
-* Ziwei Liu, Xiaoxiao Li, Ping Luo, Chen Change Loy, Xiaoou Tang, Semantic Image Segmentation via Deep Parsing Network, arXiv:1509.02634 / ICCV 2015 [[Paper]](http://arxiv.org/pdf/1509.02634.pdf) (2nd ranked in VOC 2012)
+  * Ziwei Liu, Xiaoxiao Li, Ping Luo, Chen Change Loy, Xiaoou Tang, Semantic Image Segmentation via Deep Parsing Network, arXiv:1509.02634 / ICCV 2015 [[Paper]](http://arxiv.org/pdf/1509.02634.pdf) (2nd ranked in VOC 2012)
 * CentraleSuperBoundaries, INRIA [[Paper]](http://arxiv.org/pdf/1511.07386)
-* Iasonas Kokkinos, Surpassing Humans in Boundary Detection using Deep Learning, arXiv:1411.07386 (4th ranked in VOC 2012)
+  * Iasonas Kokkinos, Surpassing Humans in Boundary Detection using Deep Learning, arXiv:1411.07386 (4th ranked in VOC 2012)
 * BoxSup [[Paper]](http://arxiv.org/pdf/1503.01640)
-* Jifeng Dai, Kaiming He, Jian Sun, BoxSup: Exploiting Bounding Boxes to Supervise Convolutional Networks for Semantic Segmentation, arXiv:1503.01640. (6th ranked in VOC2012)
+  * Jifeng Dai, Kaiming He, Jian Sun, BoxSup: Exploiting Bounding Boxes to Supervise Convolutional Networks for Semantic Segmentation, arXiv:1503.01640. (6th ranked in VOC2012)
 * POSTECH
-* Hyeonwoo Noh, Seunghoon Hong, Bohyung Han, Learning Deconvolution Network for Semantic Segmentation, arXiv:1505.04366. [[Paper]](http://arxiv.org/pdf/1505.04366) (7th ranked in VOC2012)
-* Seunghoon Hong, Hyeonwoo Noh, Bohyung Han, Decoupled Deep Neural Network for Semi-supervised Semantic Segmentation, arXiv:1506.04924. [[Paper]](http://arxiv.org/pdf/1506.04924)
+  * Hyeonwoo Noh, Seunghoon Hong, Bohyung Han, Learning Deconvolution Network for Semantic Segmentation, arXiv:1505.04366. [[Paper]](http://arxiv.org/pdf/1505.04366) (7th ranked in VOC2012)
+  * Seunghoon Hong, Hyeonwoo Noh, Bohyung Han, Decoupled Deep Neural Network for Semi-supervised Semantic Segmentation, arXiv:1506.04924. [[Paper]](http://arxiv.org/pdf/1506.04924)
+  * Seunghoon Hong,Junhyuk Oh,	Bohyung Han, and	Honglak Lee, Learning Transferrable Knowledge for Semantic Segmentation with Deep Convolutional Neural Network, arXiv:1512.07928 [[Paper](http://arxiv.org/pdf/1512.07928.pdf)] [[Project Page](http://cvlab.postech.ac.kr/research/transfernet/)]
 * Conditional Random Fields as Recurrent Neural Networks [[Paper]](http://arxiv.org/pdf/1502.03240)
-* Shuai Zheng, Sadeep Jayasumana, Bernardino Romera-Paredes, Vibhav Vineet, Zhizhong Su, Dalong Du, Chang Huang, Philip H. S. Torr, Conditional Random Fields as Recurrent Neural Networks, arXiv:1502.03240. (8th ranked in VOC2012)
+  * Shuai Zheng, Sadeep Jayasumana, Bernardino Romera-Paredes, Vibhav Vineet, Zhizhong Su, Dalong Du, Chang Huang, Philip H. S. Torr, Conditional Random Fields as Recurrent Neural Networks, arXiv:1502.03240. (8th ranked in VOC2012)
 * DeepLab
-* Liang-Chieh Chen, George Papandreou, Kevin Murphy, Alan L. Yuille, Weakly-and semi-supervised learning of a DCNN for semantic image segmentation, arXiv:1502.02734. [[Paper]](http://arxiv.org/pdf/1502.02734) (9th ranked in VOC2012)
+  * Liang-Chieh Chen, George Papandreou, Kevin Murphy, Alan L. Yuille, Weakly-and semi-supervised learning of a DCNN for semantic image segmentation, arXiv:1502.02734. [[Paper]](http://arxiv.org/pdf/1502.02734) (9th ranked in VOC2012)
 * Zoom-out [[Paper]](http://www.cv-foundation.org/openaccess/content_cvpr_2015/papers/Mostajabi_Feedforward_Semantic_Segmentation_2015_CVPR_paper.pdf)
-* Mohammadreza Mostajabi, Payman Yadollahpour, Gregory Shakhnarovich, Feedforward Semantic Segmentation With Zoom-Out Features, CVPR, 2015
+  * 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.
+  * 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)
-* Jonathan Long, Evan Shelhamer, Trevor Darrell, Fully Convolutional Networks for Semantic Segmentation, CVPR, 2015.
+  * 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.
+  * 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.
+  * 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.
 * University of Cambridge [[Web]](http://mi.eng.cam.ac.uk/projects/segnet/)
-* Vijay Badrinarayanan, Alex Kendall and Roberto Cipolla "SegNet: A Deep Convolutional Encoder-Decoder Architecture for Image Segmentation." arXiv preprint arXiv:1511.00561, 2015. [[Paper]](http://arxiv.org/abs/1511.00561)
+  * Vijay Badrinarayanan, Alex Kendall and Roberto Cipolla "SegNet: A Deep Convolutional Encoder-Decoder Architecture for Image Segmentation." arXiv preprint arXiv:1511.00561, 2015. [[Paper]](http://arxiv.org/abs/1511.00561)
 * Alex Kendall, Vijay Badrinarayanan and Roberto Cipolla "Bayesian SegNet: Model Uncertainty in Deep Convolutional Encoder-Decoder Architectures for Scene Understanding." arXiv preprint arXiv:1511.02680, 2015. [[Paper]](http://arxiv.org/abs/1511.00561)
-* POSTECH
-  * Seunghoon Hong,Junhyuk Oh,	Bohyung Han, and	Honglak Lee, Learning Transferrable Knowledge for Semantic Segmentation 
-    with Deep Convolutional Neural Network, arXiv:1512.07928 [[Paper](http://arxiv.org/pdf/1512.07928.pdf)] [[Project Page](http://cvlab.postech.ac.kr/research/transfernet/)]
 * Princeton
-* Fisher Yu, Vladlen Koltun, "Multi-Scale Context Aggregation by Dilated Convolutions", ICLR 2016, [[Paper](http://arxiv.org/pdf/1511.07122v2.pdf)]
+  * Fisher Yu, Vladlen Koltun, "Multi-Scale Context Aggregation by Dilated Convolutions", ICLR 2016, [[Paper](http://arxiv.org/pdf/1511.07122v2.pdf)]
 * Univ. of Washington, Allen AI
-* Hamid Izadinia, Fereshteh Sadeghi, Santosh Kumar Divvala, Yejin Choi, Ali Farhadi, "Segment-Phrase Table for Semantic Segmentation, Visual Entailment and Paraphrasing", ICCV, 2015, [[Paper](http://www.cv-foundation.org/openaccess/content_iccv_2015/papers/Izadinia_Segment-Phrase_Table_for_ICCV_2015_paper.pdf)]
+  * Hamid Izadinia, Fereshteh Sadeghi, Santosh Kumar Divvala, Yejin Choi, Ali Farhadi, "Segment-Phrase Table for Semantic Segmentation, Visual Entailment and Paraphrasing", ICCV, 2015, [[Paper](http://www.cv-foundation.org/openaccess/content_iccv_2015/papers/Izadinia_Segment-Phrase_Table_for_ICCV_2015_paper.pdf)]
 * INRIA
-* Iasonas Kokkinos, "Pusing the Boundaries of Boundary Detection Using deep Learning", ICLR 2016, [[Paper](http://arxiv.org/pdf/1511.07386v2.pdf)]
+  * Iasonas Kokkinos, "Pusing the Boundaries of Boundary Detection Using deep Learning", ICLR 2016, [[Paper](http://arxiv.org/pdf/1511.07386v2.pdf)]
 * UCSB
-* Niloufar Pourian, S. Karthikeyan, and B.S. Manjunath, "Weakly supervised graph based semantic segmentation by learning communities of image-parts", ICCV, 2015, [[Paper](http://www.cv-foundation.org/openaccess/content_iccv_2015/papers/Pourian_Weakly_Supervised_Graph_ICCV_2015_paper.pdf)]
+  * Niloufar Pourian, S. Karthikeyan, and B.S. Manjunath, "Weakly supervised graph based semantic segmentation by learning communities of image-parts", ICCV, 2015, [[Paper](http://www.cv-foundation.org/openaccess/content_iccv_2015/papers/Pourian_Weakly_Supervised_Graph_ICCV_2015_paper.pdf)]
 
 ### Visual Attention and Saliency
 ![saliency](https://cloud.githubusercontent.com/assets/5226447/8492362/7ec65b88-2183-11e5-978f-017e45ddba32.png)
 (from Nian Liu, Junwei Han, Dingwen Zhang, Shifeng Wen, Tianming Liu, Predicting Eye Fixations using Convolutional Neural Networks, CVPR, 2015.)
 
 * Mr-CNN [[Paper]](http://www.cv-foundation.org/openaccess/content_cvpr_2015/papers/Liu_Predicting_Eye_Fixations_2015_CVPR_paper.pdf)
-* Nian Liu, Junwei Han, Dingwen Zhang, Shifeng Wen, Tianming Liu, Predicting Eye Fixations using Convolutional Neural Networks, CVPR, 2015.
+  * Nian Liu, Junwei Han, Dingwen Zhang, Shifeng Wen, Tianming Liu, Predicting Eye Fixations using Convolutional Neural Networks, CVPR, 2015.
 * Learning a Sequential Search for Landmarks [[Paper]](http://www.cv-foundation.org/openaccess/content_cvpr_2015/papers/Singh_Learning_a_Sequential_2015_CVPR_paper.pdf)
-* Saurabh Singh, Derek Hoiem, David Forsyth, Learning a Sequential Search for Landmarks, CVPR, 2015.
+  * Saurabh Singh, Derek Hoiem, David Forsyth, Learning a Sequential Search for Landmarks, CVPR, 2015.
 * Multiple Object Recognition with Visual Attention [[Paper]](http://arxiv.org/pdf/1412.7755.pdf)
-* Jimmy Lei Ba, Volodymyr Mnih, Koray Kavukcuoglu, Multiple Object Recognition with Visual Attention, ICLR, 2015.
+  * Jimmy Lei Ba, Volodymyr Mnih, Koray Kavukcuoglu, Multiple Object Recognition with Visual Attention, ICLR, 2015.
 * Recurrent Models of Visual Attention [[Paper]](http://papers.nips.cc/paper/5542-recurrent-models-of-visual-attention.pdf)
-* Volodymyr Mnih, Nicolas Heess, Alex Graves, Koray Kavukcuoglu, Recurrent Models of Visual Attention, NIPS, 2014.
+  * Volodymyr Mnih, Nicolas Heess, Alex Graves, Koray Kavukcuoglu, Recurrent Models of Visual Attention, NIPS, 2014.
 
 ### Object Recognition
 * Weakly-supervised learning with convolutional neural networks [[Paper]](http://www.cv-foundation.org/openaccess/content_cvpr_2015/papers/Oquab_Is_Object_Localization_2015_CVPR_paper.pdf)
-* Maxime Oquab, Leon Bottou, Ivan Laptev, Josef Sivic, Is object localization for free? – Weakly-supervised learning with convolutional neural networks, CVPR, 2015.
+  * Maxime Oquab, Leon Bottou, Ivan Laptev, Josef Sivic, Is object localization for free? – Weakly-supervised learning with convolutional neural networks, CVPR, 2015.
 * FV-CNN [[Paper]](http://www.cv-foundation.org/openaccess/content_cvpr_2015/papers/Cimpoi_Deep_Filter_Banks_2015_CVPR_paper.pdf)
-* Mircea Cimpoi, Subhransu Maji, Andrea Vedaldi, Deep Filter Banks for Texture Recognition and Segmentation, CVPR, 2015.
+  * Mircea Cimpoi, Subhransu Maji, Andrea Vedaldi, Deep Filter Banks for Texture Recognition and Segmentation, CVPR, 2015.
 
 ### Understanding CNN
 ![understanding](https://cloud.githubusercontent.com/assets/5226447/8452083/1aaa0066-2023-11e5-800b-2248ead51584.PNG)
 (from Aravindh Mahendran, Andrea Vedaldi, Understanding Deep Image Representations by Inverting Them, CVPR, 2015.)
 
 * Karel Lenc, Andrea Vedaldi, Understanding image representations by measuring their equivariance and equivalence, CVPR, 2015. [[Paper]](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) 
+* 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)
 * 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)
@@ -309,61 +307,60 @@ Please feel free to [pull requests](https://github.com/kjw0612/awesome-deep-visi
 * 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
 * Surface Normal Estimation [[Paper]](http://www.cv-foundation.org/openaccess/content_cvpr_2015/papers/Wang_Designing_Deep_Networks_2015_CVPR_paper.pdf)
-* Xiaolong Wang, David F. Fouhey, Abhinav Gupta, Designing Deep Networks for Surface Normal Estimation, CVPR, 2015.
+  * Xiaolong Wang, David F. Fouhey, Abhinav Gupta, Designing Deep Networks for Surface Normal Estimation, CVPR, 2015.
 * Action Detection [[Paper]](http://www.cv-foundation.org/openaccess/content_cvpr_2015/papers/Gkioxari_Finding_Action_Tubes_2015_CVPR_paper.pdf)
-* Georgia Gkioxari, Jitendra Malik, Finding Action Tubes, CVPR, 2015.
+  * Georgia Gkioxari, Jitendra Malik, Finding Action Tubes, CVPR, 2015.
 * Crowd Counting [[Paper]](http://www.cv-foundation.org/openaccess/content_cvpr_2015/papers/Zhang_Cross-Scene_Crowd_Counting_2015_CVPR_paper.pdf)
-* Cong Zhang, Hongsheng Li, Xiaogang Wang, Xiaokang Yang, Cross-scene Crowd Counting via Deep Convolutional Neural Networks, CVPR, 2015.
+  * 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.
-* Generate image [[Paper]](http://www.cv-foundation.org/openaccess/content_cvpr_2015/papers/Dosovitskiy_Learning_to_Generate_2015_CVPR_paper.pdf)
-* Alexey Dosovitskiy, Jost Tobias Springenberg, Thomas Brox, Learning to Generate Chairs with Convolutional Neural Networks, CVPR, 2015.
+  * Fang Wang, Le Kang, Yi Li, Sketch-based 3D Shape Retrieval using Convolutional Neural Networks, CVPR, 2015.
+* Image Generation [[Paper]](http://www.cv-foundation.org/openaccess/content_cvpr_2015/papers/Dosovitskiy_Learning_to_Generate_2015_CVPR_paper.pdf)
+  * Alexey Dosovitskiy, Jost Tobias Springenberg, Thomas Brox, Learning to Generate Chairs with Convolutional Neural Networks, CVPR, 2015.
 * 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)]
-* Weakly-supervised Object Detection
-* Generate Image 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)
-* 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)]
+  * 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)
+  * 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.
+  * Leon A. Gatys, Alexander S. Ecker, Matthias Bethge, A Neural Algorithm of Artistic Style.
 * Human Gaze Estimation
-* Xucong Zhang, Yusuke Sugano, Mario Fritz, Andreas Bulling, Appearance-Based Gaze Estimation in the Wild, CVPR, 2015. [[Paper]](http://www.cv-foundation.org/openaccess/content_cvpr_2015/papers/Zhang_Appearance-Based_Gaze_Estimation_2015_CVPR_paper.pdf) [[Website]](https://www.mpi-inf.mpg.de/departments/computer-vision-and-multimodal-computing/research/gaze-based-human-computer-interaction/appearance-based-gaze-estimation-in-the-wild/)
+  * Xucong Zhang, Yusuke Sugano, Mario Fritz, Andreas Bulling, Appearance-Based Gaze Estimation in the Wild, CVPR, 2015. [[Paper]](http://www.cv-foundation.org/openaccess/content_cvpr_2015/papers/Zhang_Appearance-Based_Gaze_Estimation_2015_CVPR_paper.pdf) [[Website]](https://www.mpi-inf.mpg.de/departments/computer-vision-and-multimodal-computing/research/gaze-based-human-computer-interaction/appearance-based-gaze-estimation-in-the-wild/)
 * Face Recognition
-* Yaniv Taigman, Ming Yang, Marc'Aurelio Ranzato, Lior Wolf, DeepFace: Closing the Gap to Human-Level Performance in Face Verification, CVPR, 2014. [[Paper]](https://www.cs.toronto.edu/~ranzato/publications/taigman_cvpr14.pdf)
-* Yi Sun, Ding Liang, Xiaogang Wang, Xiaoou Tang, DeepID3: Face Recognition with Very Deep Neural Networks, 2015. [[Paper]](http://arxiv.org/abs/1502.00873)
-* Florian Schroff, Dmitry Kalenichenko, James Philbin, FaceNet: A Unified Embedding for Face Recognition and Clustering, CVPR, 2015. [[Paper]](http://arxiv.org/abs/1503.03832)
+  * Yaniv Taigman, Ming Yang, Marc'Aurelio Ranzato, Lior Wolf, DeepFace: Closing the Gap to Human-Level Performance in Face Verification, CVPR, 2014. [[Paper]](https://www.cs.toronto.edu/~ranzato/publications/taigman_cvpr14.pdf)
+  * Yi Sun, Ding Liang, Xiaogang Wang, Xiaoou Tang, DeepID3: Face Recognition with Very Deep Neural Networks, 2015. [[Paper]](http://arxiv.org/abs/1502.00873)
+  * Florian Schroff, Dmitry Kalenichenko, James Philbin, FaceNet: A Unified Embedding for Face Recognition and Clustering, CVPR, 2015. [[Paper]](http://arxiv.org/abs/1503.03832)
 * Facial Landmark Detection
-* Yue Wu, Tal Hassner, KangGeon Kim, Gerard Medioni, Prem Natarajan, Facial Landmark Detection with Tweaked Convolutional Neural Networks, 2015. [[Paper]](http://arxiv.org/abs/1511.04031) [[Project]](http://www.openu.ac.il/home/hassner/projects/tcnn_landmarks/)
+  * Yue Wu, Tal Hassner, KangGeon Kim, Gerard Medioni, Prem Natarajan, Facial Landmark Detection with Tweaked Convolutional Neural Networks, 2015. [[Paper]](http://arxiv.org/abs/1511.04031) [[Project]](http://www.openu.ac.il/home/hassner/projects/tcnn_landmarks/)
 
 ## Courses
 * Deep Vision
-* [Stanford] [CS231n: Convolutional Neural Networks for Visual Recognition](http://cs231n.stanford.edu/)
-* [CUHK] [ELEG 5040: Advanced Topics in Signal Processing(Introduction to Deep Learning)](https://piazza.com/cuhk.edu.hk/spring2015/eleg5040/home)
+  * [Stanford] [CS231n: Convolutional Neural Networks for Visual Recognition](http://cs231n.stanford.edu/)
+  * [CUHK] [ELEG 5040: Advanced Topics in Signal Processing(Introduction to Deep Learning)](https://piazza.com/cuhk.edu.hk/spring2015/eleg5040/home)
 * More Deep Learning
-* [Stanford] [CS224d: Deep Learning for Natural Language Processing](http://cs224d.stanford.edu/)
-* [Oxford] [Deep Learning by Prof. Nando de Freitas](https://www.cs.ox.ac.uk/people/nando.defreitas/machinelearning/)
-* [NYU] [Deep Learning by Prof. Yann LeCun](http://cilvr.cs.nyu.edu/doku.php?id=courses:deeplearning2014:start)
+  * [Stanford] [CS224d: Deep Learning for Natural Language Processing](http://cs224d.stanford.edu/)
+  * [Oxford] [Deep Learning by Prof. Nando de Freitas](https://www.cs.ox.ac.uk/people/nando.defreitas/machinelearning/)
+  * [NYU] [Deep Learning by Prof. Yann LeCun](http://cilvr.cs.nyu.edu/doku.php?id=courses:deeplearning2014:start)
 
 ## Books
 * Free Online Books
-* [Deep Learning by Yoshua Bengio, Ian Goodfellow and Aaron Courville](http://www.iro.umontreal.ca/~bengioy/dlbook/)
-* [Neural Networks and Deep Learning by Michael Nielsen](http://neuralnetworksanddeeplearning.com/)
-* [Deep Learning Tutorial by LISA lab, University of Montreal](http://deeplearning.net/tutorial/deeplearning.pdf)
+  * [Deep Learning by Ian Goodfellow, Yoshua Bengio, and Aaron Courville](http://www.iro.umontreal.ca/~bengioy/dlbook/)
+  * [Neural Networks and Deep Learning by Michael Nielsen](http://neuralnetworksanddeeplearning.com/)
+  * [Deep Learning Tutorial by LISA lab, University of Montreal](http://deeplearning.net/tutorial/deeplearning.pdf)
 
 ## Videos
 * Talks
-* [Deep Learning, Self-Taught Learning and Unsupervised Feature Learning By Andrew Ng](https://www.youtube.com/watch?v=n1ViNeWhC24)
-* [Recent Developments in Deep Learning By Geoff Hinton](https://www.youtube.com/watch?v=vShMxxqtDDs)
-* [The Unreasonable Effectiveness of Deep Learning by Yann LeCun](https://www.youtube.com/watch?v=sc-KbuZqGkI)
-* [Deep Learning of Representations by Yoshua bengio](https://www.youtube.com/watch?v=4xsVFLnHC_0)
+  * [Deep Learning, Self-Taught Learning and Unsupervised Feature Learning By Andrew Ng](https://www.youtube.com/watch?v=n1ViNeWhC24)
+  * [Recent Developments in Deep Learning By Geoff Hinton](https://www.youtube.com/watch?v=vShMxxqtDDs)
+  * [The Unreasonable Effectiveness of Deep Learning by Yann LeCun](https://www.youtube.com/watch?v=sc-KbuZqGkI)
+  * [Deep Learning of Representations by Yoshua bengio](https://www.youtube.com/watch?v=4xsVFLnHC_0)
 * Courses
-* [Deep Learning Course – Nando de Freitas@Oxford](http://www.computervisiontalks.com/tag/deep-learning-course/)
+  * [Deep Learning Course – Nando de Freitas@Oxford](http://www.computervisiontalks.com/tag/deep-learning-course/)
 
 ## Software
 ### Framework
@@ -376,17 +373,17 @@ Please feel free to [pull requests](https://github.com/kjw0612/awesome-deep-visi
 
 ### Applications
 * Adversarial Training
-* Code and hyperparameters for the paper "Generative Adversarial Networks" [[Web]](https://github.com/goodfeli/adversarial)
+  * Code and hyperparameters for the paper "Generative Adversarial Networks" [[Web]](https://github.com/goodfeli/adversarial)
 * Understanding and Visualizing
-* Source code for "Understanding Deep Image Representations by Inverting Them," CVPR, 2015. [[Web]](https://github.com/aravindhm/deep-goggle)
+  * Source code for "Understanding Deep Image Representations by Inverting Them," CVPR, 2015. [[Web]](https://github.com/aravindhm/deep-goggle)
 * Semantic Segmentation
-* Source code for the paper "Rich feature hierarchies for accurate object detection and semantic segmentation," CVPR, 2014. [[Web]](https://github.com/rbgirshick/rcnn)
-* Source code for the paper "Fully Convolutional Networks for Semantic Segmentation," CVPR, 2015. [[Web]](https://github.com/longjon/caffe/tree/future)
+  * Source code for the paper "Rich feature hierarchies for accurate object detection and semantic segmentation," CVPR, 2014. [[Web]](https://github.com/rbgirshick/rcnn)
+  * Source code for the paper "Fully Convolutional Networks for Semantic Segmentation," CVPR, 2015. [[Web]](https://github.com/longjon/caffe/tree/future)
 * Super-Resolution
-* Image Super-Resolution for Anime-Style-Art [[Web]](https://github.com/nagadomi/waifu2x)
+  * Image Super-Resolution for Anime-Style-Art [[Web]](https://github.com/nagadomi/waifu2x)
 * Edge Detection
-* Source code for the paper "DeepContour: A Deep Convolutional Feature Learned by Positive-Sharing Loss for Contour Detection," CVPR, 2015. [[Web]](https://github.com/shenwei1231/DeepContour)
-* Source code for the paper "Holistically-Nested Edge Detection", ICCV 2015. [[Web]](https://github.com/s9xie/hed)
+  * Source code for the paper "DeepContour: A Deep Convolutional Feature Learned by Positive-Sharing Loss for Contour Detection," CVPR, 2015. [[Web]](https://github.com/shenwei1231/DeepContour)
+  * Source code for the paper "Holistically-Nested Edge Detection", ICCV 2015. [[Web]](https://github.com/s9xie/hed)
 
 ## Tutorials
 * [CVPR 2014] [Tutorial on Deep Learning in Computer Vision](https://sites.google.com/site/deeplearningcvpr2014/)