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The ability to interact and understand the environment is a fundamental prerequisite for a wide range of applications from robotics to augmented reality.
Auto-encoding variational bayes
Diederik P Kingma and Max Welling · 2013
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Learning object deformation models for robot motion planning
Barbara Frank, Cyrill Stachniss, Rüdiger Schmedding, Matthias Teschner, and Wolfram Burgard · 2014
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Generative adversarial nets
Ian Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron Courville, and Yoshua Bengio · 2014
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Galileo: Perceiving physical object properties by integrating a physics engine with deep learning
Jiajun Wu, Ilker Yildirim, Joseph J Lim, Bill Freeman, and Josh Tenenbaum · 2015
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Generative and discriminative voxel modeling with convolutional neural networks
Andrew Brock, Theodore Lim, James M Ritchie, and Nick Weston · 2016
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Infogan: Interpretable representation learning by information maximizing generative adversarial nets
Xi Chen, Yan Duan, Rein Houthooft, John Schulman, Ilya Sutskever, and Pieter Abbeel · 2016
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Unsupervised learning for physical interaction through video prediction
Chelsea Finn, Ian Goodfellow, and Sergey Levine · 2016
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Image-to-image translation with conditional adversarial networks
Phillip Isola, Jun-Yan Zhu, Tinghui Zhou, and Alexei A Efros · 2016
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Autoencoding beyond pixels using a learned similarity metric
Anders Boesen Lindbo Larsen, Søren Kaae Sønderby, Hugo Larochelle, and Ole Winther · 2016
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Learning physical intuition of block towers by example
Adam Lerer, Sam Gross, and Rob Fergus · 2016
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Newtonian scene understanding: Unfolding the dynamics of objects in static images
Roozbeh Mottaghi, Hessam Bagherinezhad, Mohammad Rastegari, and Ali Farhadi · 2016
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Invertible conditional gans for image editing
Guim Perarnau, Joost van de Weijer, Bogdan Raducanu, and Jose M. Álvarez · 2016
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Generating videos with scene dynamics
Carl Vondrick, Hamed Pirsiavash, and Antonio Torralba · 2016
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Se3-nets: Learning rigid body motion using deep neural networks
Arunkumar Byravan and Dieter Fox · 2017
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Improved training of wasserstein gans
Ishaan Gulrajani, Faruk Ahmed, Martin Arjovsky, Vincent Dumoulin, and Aaron C Courville · 2017
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High-resolution shape completion using deep neural networks for global structure and local geometry inference
X. Han, Z. Li, H. Huang, E. Kalogerakis, and Y. Yu · 2017
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Improved adversarial systems for 3d object generation and reconstruction
Edward J. Smith and David Meger · 2017
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Learning to see physics via visual de-animation
Jiajun Wu, Erika Lu, Pushmeet Kohli, William T Freeman, and Joshua B Tenenbaum · 2017
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Differential angular imaging for material recognition
Jia Xue, Hang Zhang, Kristin Dana, and Ko Nishino · 2017
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Martín Arjovsky, Soumith Chintala, and Léon Bottou · 2017
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Cvae-gan: Fine-grained image generation through asymmetric training
Jianmin Bao, Dong Chen, Fang Wen, Houqiang Li, and Gang Hua · 2017
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Conditional generative adversarial networks for commonsense machine comprehension
Jun Zhao Bingning Wang, Kang Liu · 2017
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3d object reconstruction from a single depth view with adversarial learning
Bo Yang, Hongkai Wen, Sen Wang, Ronald Clark, Andrew Markham, and Niki Trigoni · 2017
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Unpaired image-to-image translation using cycle-consistent adversarial networks
Jun-Yan Zhu, Taesung Park, Phillip Isola, and Alexei A Efros · 2017
Later among the works it cites.