Jun-Yan Zhu, Philipp Krahenbuhl, Eli Shechtman, and Alexei A. Efros, "Generative Visual Manipulation on the Natural Image Manifold", ECCV 2016. ] ] ]
Hyeonseob Namand Bohyung Han, Learning Multi-Domain Convolutional Neural Networks for Visual Tracking, ] ] ]
Lijun Wang, Wanli Ouyang, Xiaogang Wang, and Huchuan Lu, Visual Tracking with fully Convolutional Networks, ICCV 2015 ] ]
Chao Ma, Jia-Bin Huang, Xiaokang Yang and Ming-Hsuan Yang, Hierarchical Convolutional Features for Visual Tracking, ICCV 2015 ] ]
Karol Gregor, Ivo Danihelka, Alex Graves, Danilo Jimenez Rezende, Daan Wierstra, "DRAW: A Recurrent Neural Network For Image Generation", ICML, 2015. ]
Takeru Miyato, Shin-ichi Maeda, Masanori Koyama, Ken Nakae, Shin Ishii, "Distributional Smoothing with Virtual Adversarial Training", ICLR 2016, ]
Harrison Edwards, Amos Storkey, "Censoring Representations with an Adversary", ICLR 2016, ]
Jost Tobias Springenberg, "Unsupervised and Semi-supervised Learning with Categorical Generative Adversarial Networks", ICLR 2016, ]
Elman Mansimov, Emilio Parisotto, Jimmy Ba, Ruslan Salakhutdinov, "Generating Images from Captions with Attention", ICLR 2016, ]
Zhenwen Dai, Andreas Damianou, Javier Gonzalez, Neil Lawrence, "Variationally Auto-Encoded Deep Gaussian Processes", ICLR 2016. ]
SNU + NAVER ]
UC Berkeley + Sony ]
Postech ]
Lucas Theis, Aäron van den Oord, Matthias Bethge, "A note on the evaluation of generative models", ICLR 2016. ]
Jun-Yan Zhu, Philipp Krahenbuhl, Eli Shechtman, and Alexei A. Efros, "Generative Visual Manipulation on the Natural Image Manifold", ECCV 2016. ] ] ]
Alec Radford, Luke Metz, Soumith Chintala, "Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks", ICLR 2016. ]
Visual Analogy ]
Samaneh Azadi, Jiashi Feng, Stefanie Jegelka, Trevor Darrell, "Auxiliary Image Regularization for Deep CNNs with Noisy Labels", ICLR 2016, ]
Lijun Wang, Wanli Ouyang, Xiaogang Wang, and Huchuan Lu, Visual Tracking with fully Convolutional Networks, ICCV 2015 ] ]
Chao Ma, Jia-Bin Huang, Xiaokang Yang and Ming-Hsuan Yang, Hierarchical Convolutional Features for Visual Tracking, ICCV 2015 ] ]
Michael Mathieu, camille couprie, Yann Lecun, "Deep Multi Scale Video Prediction Beyond Mean Square Error", ICLR 2016. ]
Nicolas Ballas, Li Yao, Pal Chris, Aaron Courville, "Delving Deeper into Convolutional Networks for Learning Video Representations", ICLR 2016. ]
Weakly Supervised Object Localization with Multi-fold Multiple Instance Learning ]
Microsoft (Deep Residual Learning) ]]
Univ. Montreal / Univ. Toronto ] ]
Hyeonseob Namand Bohyung Han, Learning Multi-Domain Convolutional Neural Networks for Visual Tracking, ] ] ]
Seunghoon Hong,Junhyuk Oh, Bohyung Han, and Honglak Lee, Learning Transferrable Knowledge for Semantic Segmentation with Deep Convolutional Neural Network, arXiv:1512.07928 ] ]
Fisher Yu, Vladlen Koltun, "Multi-Scale Context Aggregation by Dilated Convolutions", ICLR 2016, ]
Hamid Izadinia, Fereshteh Sadeghi, Santosh Kumar Divvala, Yejin Choi, Ali Farhadi, "Segment-Phrase Table for Semantic Segmentation, Visual Entailment and Paraphrasing", ICCV, 2015, ]
Iasonas Kokkinos, "Pusing the Boundaries of Boundary Detection Using deep Learning", ICLR 2016, ]
Niloufar Pourian, S. Karthikeyan, and B.S. Manjunath, "Weakly supervised graph based semantic segmentation by learning communities of image-parts", ICCV, 2015, ]
SNU + NAVER ]
Idiap / EPFL / Facebook ]
UCLA / Baidu ]
Jacob Devlin, Saurabh Gupta, Ross Girshick, Margaret Mitchell, C. Lawrence Zitnick, Exploring Nearest Neighbor Approaches for Image Captioning, arXiv:1505.04467 ]
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 ]
Adelaide ]
Tilburg ]
Univ. Montreal ]
Cornell ]
MS + City Univ. of HongKong ]
Univ. Montreal / Univ. Sherbrooke ]
MPI / Berkeley ]
Univ. Toronto / MIT ]
MetaMind ]
Univ. Montreal ]
POSTECH ] ]
CMU / Microsoft Research ]
TAU / USC ]
POSTECH ] ]
Seunghoon Hong,Junhyuk Oh, Bohyung Han, and Honglak Lee, Learning Transferrable Knowledge for Semantic Segmentation with Deep Convolutional Neural Network, arXiv:1512.07928 ] ]
Hyeonseob Namand Bohyung Han, Learning Multi-Domain Convolutional Neural Networks for Visual Tracking, ] ] ]
Microsoft (Deep Residual Learning) ]]
Jun-Yan Zhu, Philipp Krahenbuhl, Eli Shechtman, and Alexei A. Efros, "Generative Visual Manipulation on the Natural Image Manifold", ECCV 2016. ] ] ]
Univ. Montreal / Univ. Toronto ] ]
Theano-based deep learning libraries: ], ], ], ]
Theano-based deep learning libraries: ], ], ], ]
Theano-based deep learning libraries: ], ], ], ]
Theano-based deep learning libraries: ], ], ], ]
Torch-based deep learning libraries: ],
MatConvNet: CNNs for MATLAB ]
MXNet: A flexible and efficient deep learning library for heterogeneous distributed systems with multi-language support ]
Deepgaze: A computer vision library for human-computer interaction based on CNNs ]
Theano: Mathematical library in Python, maintained by LISA lab ]
Caffe: Deep learning framework by the BVLC ]
Torch7: Deep learning library in Lua, used by Facebook and Google Deepmind ]
Tensorflow: An open source software library for numerical computation using data flow graph by Google ]