Goldsmiths - University of London

Autoencoding Blade Runner: Reconstructing Films With Artificial Neural Networks

Broad, Terence and Grierson, Mick. 2017. Autoencoding Blade Runner: Reconstructing Films With Artificial Neural Networks. SIGGRAPH 17 Art Papers, [Article] (In Press)

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Abstract or Description

‘Blade Runner—Autoencoded’ is 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 project explores the aesthetic qualities of the disembodied gaze of the neural network. The autoencoder is also capable of representing images from films it has not seen based on what it has learned from watching Blade Runner.

Item Type: Article

Departments, Centres and Research Units:

Computing > Embodied AudioVisual Interaction Group (EAVI)


14 March 2017Accepted
1 August 2017Published Online

Item ID:


Date Deposited:

12 Jun 2017 10:47

Last Modified:

03 Nov 2017 15:47

Peer Reviewed:

Yes, this version has been peer-reviewed.

URI: http://research.gold.ac.uk/id/eprint/20555

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