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It takes (only) two: adversarial generator-encoder networks

Abstract:

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
Version:
Accepted Manuscript

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Institution:
University of Oxford
Division:
MPLS Division
Department:
Engineering Science
Oxford college:
New College; New College; New College; New College
Role:
Author
Publisher:
Association for the Advancement of Artificial Intelligence Publisher's website
Pages:
1250-1257
Publication date:
2018-04-25
Acceptance date:
2017-11-08
EISSN:
2374-3468
Pubs id:
pubs:948558
URN:
uri:3be3099a-b8c5-4c61-a39c-c5c950a42a6d
UUID:
uuid:3be3099a-b8c5-4c61-a39c-c5c950a42a6d
Local pid:
pubs:948558
ISBN:
9781577358008

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