Reducing DNN labelling cost using surprise adequacy: an industrial case study for autonomous driving

Cited 0 time in webofscience Cited 7 time in scopus
  • Hit : 207
  • Download : 0
DC FieldValueLanguage
dc.contributor.authorKim, Jinhanko
dc.contributor.authorJu, Jeongilko
dc.contributor.authorFeldt, Robertko
dc.contributor.authorYoo, Shinko
dc.date.accessioned2020-11-11T05:55:25Z-
dc.date.available2020-11-11T05:55:25Z-
dc.date.created2020-11-09-
dc.date.issued2020-11-10-
dc.identifier.citationACM Joint Meeting on European Software Engineering Conference and Symposium on the Foundations of Software Engineering, pp.1466 - 1476-
dc.identifier.urihttp://hdl.handle.net/10203/277216-
dc.languageEnglish-
dc.publisherACM SIGSOFT-
dc.titleReducing DNN labelling cost using surprise adequacy: an industrial case study for autonomous driving-
dc.typeConference-
dc.type.rimsCONF-
dc.citation.beginningpage1466-
dc.citation.endingpage1476-
dc.citation.publicationnameACM Joint Meeting on European Software Engineering Conference and Symposium on the Foundations of Software Engineering-
dc.identifier.conferencecountryUS-
dc.identifier.conferencelocationVirtual-
dc.identifier.doi10.1145/3368089.3417065-
dc.contributor.localauthorYoo, Shin-
dc.contributor.nonIdAuthorJu, Jeongil-
dc.contributor.nonIdAuthorFeldt, Robert-
Appears in Collection
CS-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