Understanding Cross-Domain Few-Shot Learning Based on Domain Similarity and Few-Shot Difficulty

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dc.contributor.authorOh, Jae Hoonko
dc.contributor.authorKim, Sungnyunko
dc.contributor.authorHo, Namgyuko
dc.contributor.authorKim, Jin-Hwako
dc.contributor.authorSong, Hwanjunko
dc.contributor.authorYun, Seyoungko
dc.date.accessioned2022-12-07T10:00:22Z-
dc.date.available2022-12-07T10:00:22Z-
dc.date.created2022-12-03-
dc.date.created2022-12-03-
dc.date.created2022-12-03-
dc.date.issued2022-12-01-
dc.identifier.citation36th Conference on Neural Information Processing Systems, NeurIPS 2022-
dc.identifier.urihttp://hdl.handle.net/10203/301976-
dc.languageEnglish-
dc.publisherAdvances in Neural Information Processing Systems (NeurIPS)-
dc.titleUnderstanding Cross-Domain Few-Shot Learning Based on Domain Similarity and Few-Shot Difficulty-
dc.typeConference-
dc.identifier.scopusid2-s2.0-85163144802-
dc.type.rimsCONF-
dc.citation.publicationname36th Conference on Neural Information Processing Systems, NeurIPS 2022-
dc.identifier.conferencecountryUS-
dc.identifier.conferencelocationThe New Orleans Convention Center-
dc.contributor.localauthorYun, Seyoung-
dc.contributor.nonIdAuthorHo, Namgyu-
dc.contributor.nonIdAuthorKim, Jin-Hwa-
dc.contributor.nonIdAuthorSong, Hwanjun-
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AI-Conference Papers(학술대회논문)
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