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dc.contributor.authorBau, D
dc.contributor.authorLiu, S
dc.contributor.authorWang, T
dc.contributor.authorZhu, JY
dc.contributor.authorTorralba, A
dc.date.accessioned2021-11-05T19:21:45Z
dc.date.available2021-11-05T19:21:45Z
dc.date.issued2020
dc.identifier.urihttps://hdl.handle.net/1721.1/137596
dc.description.abstract© 2020, Springer Nature Switzerland AG. A deep generative model such as a GAN learns to model a rich set of semantic and physical rules about the target distribution, but up to now, it has been obscure how such rules are encoded in the network, or how a rule could be changed. In this paper, we introduce a new problem setting: manipulation of specific rules encoded by a deep generative model. To address the problem, we propose a formulation in which the desired rule is changed by manipulating a layer of a deep network as a linear associative memory. We derive an algorithm for modifying one entry of the associative memory, and we demonstrate that several interesting structural rules can be located and modified within the layers of state-of-the-art generative models. We present a user interface to enable users to interactively change the rules of a generative model to achieve desired effects, and we show several proof-of-concept applications. Finally, results on multiple datasets demonstrate the advantage of our method against standard fine-tuning methods and edit transfer algorithms.en_US
dc.language.isoen
dc.publisherSpringer International Publishingen_US
dc.relation.isversionof10.1007/978-3-030-58452-8_21en_US
dc.rightsCreative Commons Attribution-Noncommercial-Share Alikeen_US
dc.rights.urihttp://creativecommons.org/licenses/by-nc-sa/4.0/en_US
dc.sourcearXiven_US
dc.titleRewriting a Deep Generative Modelen_US
dc.typeArticleen_US
dc.identifier.citationBau, D, Liu, S, Wang, T, Zhu, JY and Torralba, A. 2020. "Rewriting a Deep Generative Model." Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 12346 LNCS.
dc.contributor.departmentMassachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory
dc.contributor.departmentMassachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
dc.relation.journalLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)en_US
dc.eprint.versionOriginal manuscripten_US
dc.type.urihttp://purl.org/eprint/type/ConferencePaperen_US
eprint.statushttp://purl.org/eprint/status/NonPeerRevieweden_US
dc.date.updated2021-01-28T16:05:27Z
dspace.orderedauthorsBau, D; Liu, S; Wang, T; Zhu, JY; Torralba, Aen_US
dspace.date.submission2021-01-28T16:05:33Z
mit.journal.volume12346 LNCSen_US
mit.licenseOPEN_ACCESS_POLICY
mit.metadata.statusAuthority Work and Publication Information Neededen_US


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