Semi-supervised out-of-distribution detection and fine-tuning method반지도 학습을 활용한 이상 탐지와 이상 데이터를 활용한 미세조정

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As the importance of data continues to grow, out of distribution detection is becoming increasingly crucial in various fields. In this thesis, we aim to address the escalating significance of anomaly detection within the context of realistic scenarios. Specifically, we focus on a methodology that utilizes a semi-supervised learning model for anomaly detection. Additionally, we explore approaches to enhance detection performance through fine-tuning when provided with a limited amount of out of distribution data.
Advisors
신하용researcher
Description
한국과학기술원 :산업및시스템공학과,
Publisher
한국과학기술원
Issue Date
2024
Identifier
325007
Language
eng
Description

학위논문(석사) - 한국과학기술원 : 산업및시스템공학과, 2024.2,[iii, 20p :]

Keywords

준지도 학습▼a마할라노비스 거리▼a이상탐지▼a미세조정; Semi-supervised learning▼aMahalanobis distance▼aOut of distribution decteion▼aFine-tune

URI
http://hdl.handle.net/10203/321467
Link
http://library.kaist.ac.kr/search/detail/view.do?bibCtrlNo=1096684&flag=dissertation
Appears in Collection
IE-Theses_Master(석사논문)
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