Conference item
Learning disentangled representations with semi-supervised deep generative models
- Abstract:
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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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- Files:
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(Accepted manuscript, pdf, 2.1MB)
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Authors
Funding
+ Engineering and Physical Sciences Research Council
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Funding agency for:
Paige, B
Grant:
EP/N510129/1
+ Defense Advanced Research Projects Agency
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Funding agency for:
Van De Meent, J
Grant:
FA8750-14-2-0006
+ Engineering and Physical Sciences Research Council
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Funding agency for:
Wood, F
Grant:
FA8750-17-2-0093
+ Defense Advanced Research Projects Agency
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Funding agency for:
Goodman, N
Grant:
FA8750-14-2-0006
+ Northeastern University
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Funding agency for:
Van De Meent, J
Grant:
FA8750-14-2-0006
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Bibliographic Details
- 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
Item Description
- Pubs id:
-
pubs:854010
- UUID:
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uuid:128e9b3b-b303-4bdf-a849-72cab89b3635
- Local pid:
- pubs:854010
- Deposit date:
- 2018-06-22
Terms of use
- Copyright holder:
- © (2017) Siddharth, et al and NIPS All rights reserved
- Copyright date:
- 2018
- Notes:
- This is the author accepted manuscript following peer review version of the article. The final version is available online from Curran Associates
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