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Autoencoding Blade Runner
Author(s) -
Terence Broad,
Mick Grierson
Publication year - 2017
Publication title -
university of the arts london research online (university of the arts london)
Language(s) - English
Resource type - Conference proceedings
ISBN - 978-1-4503-4998-7
DOI - 10.1145/3072940.3072964
Subject(s) - autoencoder , blade (archaeology) , gaze , computer science , frame (networking) , artificial intelligence , artificial neural network , style (visual arts) , generative grammar , computer vision , natural language processing , visual arts , engineering , mechanical engineering , art , telecommunications
In this paper, the authors explain how they created Blade Runner---Autoencoded, a film made by training an autoencoder---a type of generative neural network---to recreate frames from the film Blade Runner. The autoencoder is made to reinterpret every individual frame, reconstructing it based on its memory of the film. The result is a hazy, dreamlike version of the original film. The authors discuss how the project explores the aesthetic qualities of the disembodied gaze of the neural network and describe how the autoencoder is also capable of reinterpreting films it has not been trained on, transferring the visual style it has learned from watching Blade Runner (1982).

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