Neural scene representation and rendering

Author:

Eslami S. M. Ali1ORCID,Jimenez Rezende Danilo1ORCID,Besse Frederic1ORCID,Viola Fabio1ORCID,Morcos Ari S.1ORCID,Garnelo Marta1,Ruderman Avraham1ORCID,Rusu Andrei A.1ORCID,Danihelka Ivo1,Gregor Karol1,Reichert David P.1,Buesing Lars1,Weber Theophane1ORCID,Vinyals Oriol1,Rosenbaum Dan1,Rabinowitz Neil1,King Helen1,Hillier Chloe1,Botvinick Matt1ORCID,Wierstra Daan1,Kavukcuoglu Koray1,Hassabis Demis1

Affiliation:

1. DeepMind, 5 New Street Square, London EC4A 3TW, UK.

Abstract

A scene-internalizing computer program To train a computer to “recognize” elements of a scene supplied by its visual sensors, computer scientists typically use millions of images painstakingly labeled by humans. Eslami et al. developed an artificial vision system, dubbed the Generative Query Network (GQN), that has no need for such labeled data. Instead, the GQN first uses images taken from different viewpoints and creates an abstract description of the scene, learning its essentials. Next, on the basis of this representation, the network predicts what the scene would look like from a new, arbitrary viewpoint. Science , this issue p. 1204

Publisher

American Association for the Advancement of Science (AAAS)

Subject

Multidisciplinary

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