Conference item
It takes (only) two: adversarial generator-encoder networks
- Abstract:
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We present a new autoencoder-type architecture that is trainable in an unsupervised mode, sustains both generation and inference, and has the quality of conditional and unconditional samples boosted by adversarial learning. Unlike previous hybrids of autoencoders and adversarial networks, the adversarial game in our approach is set up directly between the encoder and the generator, and no external mappings are trained in the process of learning. The game objective compares the divergences of ...
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- Publication status:
- Published
- Peer review status:
- Peer reviewed
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(Accepted manuscript, pdf, 3.9MB)
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Authors
Funding
Bibliographic Details
- Publisher:
- Association for the Advancement of Artificial Intelligence Publisher's website
- Journal:
- Association for the Advancement of Artificial Intelligence (AAAI), 2018 Journal website
- Pages:
- 1250-1257
- Host title:
- Association for the Advancement of Artificial Intelligence (AAAI), 2018: Thirty Second AAAAI Conference on Artificial Intelligence, New Orleans, Louisiana USA — February 2–7, 2018
- Publication date:
- 2018-04-25
- Acceptance date:
- 2017-11-08
- EISSN:
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2374-3468
- Source identifiers:
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948558
- ISBN:
- 9781577358008
Item Description
- Pubs id:
-
pubs:948558
- UUID:
-
uuid:3be3099a-b8c5-4c61-a39c-c5c950a42a6d
- Local pid:
- pubs:948558
- Deposit date:
- 2018-11-30
Terms of use
- Copyright holder:
- Association for the Advancement of Artificial Intelligence
- Copyright date:
- 2018
- Notes:
- © 2018, Association for the Advancement of Artificial Intelligence. This is the author accepted manuscript following peer review version of the article. The final version is available online from Association for the Advancement of Artificial Intelligence
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