Deep Convolutional Inverse Graphics Network

Keywords

Computer science, Artificial intelligence, Graphics, Rendering (computer graphics), Convolution (computer science)

References

• Deep Boltzmann MachinesRuslan Salakhutdinov, Geoffrey E. Hinton — 2009
• Disentangling Factors of Variation via Generative EntanglingGuillaume Desjardins, Aaron Courville, Yoshua Bengio — 2012
• Inverse Graphics with Probabilistic CAD ModelsTejas D. Kulkarni, Vikash K. Mansinghka, Pushmeet Kohli — 2014
• Deep Lambertian NetworksYichuan Tang, Ruslan Salakhutdinov, Geoffrey E. Hinton — 2012
• Learning to generate chairs with convolutional neural networksAlexey Dosovitskiy, Jost Tobias Springenberg, Thomas Brox — 2014
• Auto-Encoding Variational BayesDiederik P. Kingma, Max Welling — 2013
• Picture: A probabilistic programming language for scene perceptionTejas D. Kulkarni, Pushmeet Kohli, Joshua B. Tenenbaum — 2015
• MatConvNet: Convolutional Neural Networks for MATLABAndrea Vedaldi, Karel Lenc — 2014
• Seeing 3D Chairs: Exemplar Part-Based 2D-3D Alignment Using a Large Dataset of CAD ModelsMathieu Aubry, Daniel Maturana, Alexei A. Efros — 2014
• Approximate Bayesian computation (ABC) gives exact results under the assumption of model errorR. Wilkinson — 2008
• Learning Deep Architectures for AIYoshua Bengio — 2007
• Measuring Invariances in Deep NetworksIan Goodfellow, Honglak Lee, Quoc V. Le — 2009
• A 3D Face Model for Pose and Illumination Invariant Face RecognitionPascal Paysan, Reinhard Knothe, Brian Amberg — 2009
• The Role of Context for Object Detection and Semantic Segmentation in the WildRoozbeh Mottaghi, Xianjie Chen, Xiaobai Liu — 2014
• Deep Generative Stochastic Networks Trainable by BackpropYoshua Bengio, Eric Laufer, Guillaume Alain — 2013
• Convolutional deep belief networks for scalable unsupervised learning of hierarchical representationsHonglak Lee, Roger Grosse, Rajesh Ranganath — 2009
• Approximate Bayesian Image Interpretation using Generative Probabilistic Graphics ProgramsVikash K. Mansinghka, Tejas D. Kulkarni, Yura Perov — 2013
• Learning the Irreducible Representations of Commutative Lie GroupsTaco Cohen, Max Welling — 2014
• A Fast Learning Algorithm for Deep Belief NetsGeoffrey E. Hinton, Simon Osindero, Yee‐Whye Teh — 2006
• Unsupervised Learning of Invariant Feature Hierarchies with Applications to Object RecognitionMarc’Aurelio Ranzato, Fu Jie Huang, Y-Lan Boureau — 2007

Cited by

• Embed to Control: A Locally Linear Latent Dynamics Model for Control from Raw ImagesManuel Watter, Jost Tobias Springenberg, Joschka Boedecker — 2015
• Automatic Relevance Determination For Deep Generative ModelsTheofanis Karaletsos, Gunnar Rätsch — 2015
• Understanding Deep Features with Computer-Generated ImageryMathieu Aubry, Bryan Russell — 2015
• Deep Stereo: Learning to Predict New Views from the World's ImageryJohn P. Flynn, Ivan Neulander, James Philbin — 2015
• Learning image representations equivariant to ego-motionDinesh Jayaraman, Kristen Grauman — 2015
• See the Difference: Direct Pre-Image Reconstruction and Pose Estimation by Differentiating HOGWei-Chen Chiu, Mario Fritz — 2015
• Picture: A probabilistic programming language for scene perceptionTejas D. Kulkarni, Pushmeet Kohli, Joshua B. Tenenbaum — 2015
• Unsupervised Learning of Visual Structure using Predictive Generative NetworksWilliam Lotter, Gabriel Kreiman, David Cox — 2015
• Efficient inference in occlusion-aware generative models of imagesJonathan Huang, Kevin Murphy — 2015
• Towards Automatic Image Editing: Learning to See another YouAmir Ghodrati, Xu Jia, Marco Pedersoli — 2015
• Deep Visual Analogy-MakingScott Reed, Yi Zhang, Yuting Zhang — 2015
• Learning FRAME Models Using CNN FiltersYang Lu, Song‐Chun Zhu, Ying Nian Wu — 2015
• Attribute2Image: Conditional Image Generation from Visual AttributesXinchen Yan, Yang, Jimei, Kihyuk Sohn — 2015
• Learning Image Representations Tied to Ego-MotionDinesh Jayaraman, Kristen Grauman — 2015
• Training a Feedback Loop for Hand Pose EstimationMarkus Oberweger, Paul Wohlhart, Vincent Lepetit — 2015
• A Taxonomy of Deep Convolutional Neural Nets for Computer VisionSuraj Srinivas, Ravi Kiran Sarvadevabhatla, Konda Reddy Mopuri — 2016
• Scene Intrinsics and Depth from a Single ImageEvan Shelhamer, Jonathan T. Barron, Trevor Darrell — 2015
• Denoising without access to clean data using a partitioned autoencoderDan Stowell, Richard E. Turner — 2015
• Learning FRAME Models Using CNN Filters for Knowledge VisualizationYang Lu, Song‐Chun Zhu, Ying Wu — 2015
• Single-view to Multi-view: Reconstructing Unseen Views with a Convolutional NetworkMaxim Tatarchenko, Alexey Dosovitskiy, Thomas Brox — 2015

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