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Many machine vision applications, such as semantic segmentation and depth prediction, require predictions for every pixel of the input image.
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Adaptive deconvolutional networks for mid and high level feature learning
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Nathan Silberman, Derek Hoiem, Pushmeet Kohli, and Rob Fergus · 2012
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Generative adversarial nets
Ian J. Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron C. Courville, and Yoshua Bengio · 2014
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Predicting depth, surface normals and semantic labels with a common multi-scale convolutional architecture
David Eigen and Rob Fergus · 2015
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Batch normalization: Accelerating deep network training by reducing internal covariate shift
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Alex Kendall, Vijay Badrinarayanan, and Roberto Cipolla · 2015
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Ziwei Liu, Ping Luo, Xiaogang Wang, and Xiaoou Tang · 2015
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Olga Russakovsky, Jia Deng, Hao Su, Jonathan Krause, Sanjeev Satheesh, Sean Ma, Zhiheng Huang, Andrej Karpathy, Aditya Khosla, Michael Bernstein, Alexander C. Berg, and Li Fei-Fei · 2015
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Real-time single image and video super-resolution using an efficient sub-pixel convolutional neural network
Wenzhe Shi, Jose Caballero, Ferenc Huszár, Johannes Totz, Andrew P Aitken, Rob Bishop, Daniel Rueckert, and Zehan Wang · 2016
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Christian Szegedy, Vincent Vanhoucke, Sergey Ioffe, Jon Shlens, and Zbigniew Wojna · 2016
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Ultra-resolving face images by discriminative generative networks
Xin Yu and Fatih Porikli · 2016
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Richard Zhang, Phillip Isola, and Alexei A Efros · 2016
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BEGAN: Boundary equilibrium generative adversarial networks
David Berthelot, Tom Schumm, and Luke Metz · 2017
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Deep residual learning for image recognition
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