Deep convolutional inverse graphics network

Deep convolutional inverse graphics network

This paper presents the Deep Convolution Inverse Graphics Network (DC-IGN), a model that aims to learn an interpretable representation of images, disentangled with respect to three-dimensional scene structure and viewing transformations such as depth rotations and lighting variations. The DC-IGN mod...

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Bibliographic Details
Main Authors: Kohli, Pushmeet, Kulkarni, Tejas Dattatraya, Whitney, William F., Tenenbaum, Joshua B
Other Authors: Massachusetts Institute of Technology. Department of Brain and Cognitive Sciences
Format: Article
Published: Neural Information Processing Systems Foundation, Inc 2017
Online Access:http://hdl.handle.net/1721.1/112752
https://orcid.org/0000-0002-7077-2765
https://orcid.org/0000-0002-0628-6789
https://orcid.org/0000-0002-1925-2035

Internet

http://hdl.handle.net/1721.1/112752
https://orcid.org/0000-0002-7077-2765
https://orcid.org/0000-0002-0628-6789
https://orcid.org/0000-0002-1925-2035

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