Generative classifier for deep discriminative classifier : novelty detection and noisy labels = 딥 분별 분류기를 위한 생성 분류기 : 노벌티 탐지 및 노이지 레이블novelty detection and noisy labels

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dc.contributor.advisorShin, Jinwoo-
dc.contributor.advisor신진우-
dc.contributor.authorLee, Kimin-
dc.date.accessioned2021-05-12T19:41:01Z-
dc.date.available2021-05-12T19:41:01Z-
dc.date.issued2020-
dc.identifier.urihttp://library.kaist.ac.kr/search/detail/view.do?bibCtrlNo=909441&flag=dissertationen_US
dc.identifier.urihttp://hdl.handle.net/10203/284205-
dc.description학위논문(박사) - 한국과학기술원 : 전기및전자공학부, 2020.2,[vi, 47p :]-
dc.description.abstractIn recent years, deep generative models have been largely dismissed for fully-supervised classification settings as they are often substantially outperformed by deep discriminative classifiers (e.g., softmax classifiers). In contrast to this common belief, this thesis shows that it is possible to formulate a simple generative classifier that is useful in detecting abnormal samples (i.e., novelty detection) and handling training samples with (incorrect) noisy labels without much sacrifice of the original discriminative per- formance with respect to in-distribution or/and clean labeled data. We believe that our approach have a potential to apply to many other related machine learning tasks, e.g., active learning, ensemble learning, and few-shot learning.-
dc.languageeng-
dc.publisher한국과학기술원-
dc.subjectDeep generative classifier▼anovelty detection▼anoisy labels-
dc.subject딥생성분류기▼a노벌티탐지▼a노이지레이블-
dc.titleGenerative classifier for deep discriminative classifier : novelty detection and noisy labels = 딥 분별 분류기를 위한 생성 분류기 : 노벌티 탐지 및 노이지 레이블-
dc.title.alternativenovelty detection and noisy labels-
dc.typeThesis(Ph.D)-
dc.identifier.CNRN325007-
dc.description.department한국과학기술원 :전기및전자공학부,-
dc.contributor.alternativeauthor이기민-
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