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Learning disentangled representations with semi-supervised deep generative models

Abstract:

Variational autoencoders (VAEs) learn representations of data by jointly training a probabilistic encoder and decoder network. Typically these models encode all features of the data into a single variable. Here we are interested in learning disentangled representations that encode distinct aspects of the data into separate variables. We propose to learn such representations using model architectures that generalise from standard VAEs, employing a general graphical model structure in the enco...

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Publication status:
Published
Peer review status:
Peer reviewed

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Institution:
University of Oxford
Division:
MPLS Division
Department:
Engineering Science
Role:
Author
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Institution:
University of Oxford
Division:
MPLS Division
Department:
Engineering Science
Role:
Author
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Funding agency for:
Paige, B
Grant:
EP/N510129/1
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Funding agency for:
Van De Meent, J
Grant:
FA8750-14-2-0006
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Funding agency for:
Wood, F
Grant:
FA8750-17-2-0093
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Funding agency for:
Goodman, N
Grant:
FA8750-14-2-0006
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Funding agency for:
Van De Meent, J
Grant:
FA8750-14-2-0006
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Publisher:
Curran Associates Publisher's website
Journal:
Advances in Neural Information Processing Systems Journal website
Volume:
30
Pages:
5927-5937
Host title:
Advances in Neural Information Processing Systems 30: 31st Annual Conference on Neural Information Processing Systems (NIPS 2017)
Publication date:
2018-06-01
Acceptance date:
2017-09-04
Source identifiers:
854010
ISBN:
9781510860964
Pubs id:
pubs:854010
UUID:
uuid:128e9b3b-b303-4bdf-a849-72cab89b3635
Local pid:
pubs:854010
Deposit date:
2018-06-22

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