Regularizing Feature Distribution using Sliced Wasserstein Distance for Semi-Supervised Learning

Cited 0 time in webofscience Cited 0 time in scopus
  • Hit : 153
  • Download : 0
DC FieldValueLanguage
dc.contributor.authorKim, Jinhyungko
dc.contributor.authorLee, Chanhoko
dc.contributor.authorKim, Junmoko
dc.date.accessioned2018-12-20T01:58:52Z-
dc.date.available2018-12-20T01:58:52Z-
dc.date.created2018-11-29-
dc.date.created2018-11-29-
dc.date.issued2018-11-19-
dc.identifier.citationThe 12th Multi-disciplinary International Conference on Artificial Intelligence (MIWAI 2018)-
dc.identifier.urihttp://hdl.handle.net/10203/247256-
dc.languageEnglish-
dc.publisherMahasarakham University, with association of Vietnam Academy of Science and Technology and University of Science and Technology-
dc.titleRegularizing Feature Distribution using Sliced Wasserstein Distance for Semi-Supervised Learning-
dc.typeConference-
dc.identifier.scopusid2-s2.0-85057084065-
dc.type.rimsCONF-
dc.citation.publicationnameThe 12th Multi-disciplinary International Conference on Artificial Intelligence (MIWAI 2018)-
dc.identifier.conferencecountryVN-
dc.identifier.conferencelocationFlower garden hotel, Hanoi-
dc.contributor.localauthorKim, Junmo-
Appears in Collection
EE-Conference Papers(학술회의논문)
Files in This Item
There are no files associated with this item.

qr_code

  • mendeley

    citeulike


rss_1.0 rss_2.0 atom_1.0