On Adversarial Mixup Resynthesis

Chris Beckham, Sina Honari, Vikas Verma, Alex Lamb, Farnoosh Ghadiri, R. Devon Hjelm, Yoshua Bengio, Chris Pal

Tutkimustuotos: Artikkeli kirjassa/konferenssijulkaisussaConference article in proceedingsScientificvertaisarvioitu

Abstrakti

In this paper, we explore new approaches to combining information encoded within the learned representations of autoencoders. We explore models that are capable of combining the attributes of multiple inputs such that a resynthesised output is trained to fool an adversarial discriminator for real versus synthesised data. Furthermore, we explore the use of such an architecture in the context of semi-supervised learning, where we learn a mixing function whose objective is to produce interpolations of hidden states, or masked combinations of latent representations that are consistent with a conditioned class label. We show quantitative and qualitative evidence that such a formulation is an interesting avenue of research.
AlkuperäiskieliEnglanti
Otsikko33rd Conference on Neural Information Processing Systems
AlaotsikkoNeurIPS 2019
KustantajaNeural Information Processing Systems Foundation
TilaJulkaistu - 2019
OKM-julkaisutyyppiA4 Artikkeli konferenssijulkaisussa
TapahtumaConference on Neural Information Processing Systems - Vancouver, Kanada
Kesto: 8 jouluk. 201914 jouluk. 2019
Konferenssinumero: 33
https://neurips.cc

Julkaisusarja

NimiAdvances in Neural Information Processing Systems
ISSN (elektroninen)1049-5258

Conference

ConferenceConference on Neural Information Processing Systems
LyhennettäNeurIPS
Maa/AlueKanada
KaupunkiVancouver
Ajanjakso08/12/201914/12/2019
www-osoite

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