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Image Synthesis for Self-supervised Visual Representation Learning
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| 자료유형 | E-Book |
|---|---|
| 개인저자 | Zhang, Richard. |
| 단체저자명 | University of California, Berkeley. Electrical Engineering & Computer Sciences. |
| 서명/저자사항 | Image Synthesis for Self-supervised Visual Representation Learning. |
| 발행사항 | [S.l.] : University of California, Berkeley., 2018 |
| 발행사항 | Ann Arbor : ProQuest Dissertations & Theses, 2018 |
| 형태사항 | 138 p. |
| 소장본 주기 | School code: 0028. |
| ISBN | 9780438324800 |
| 일반주기 |
Source: Dissertation Abstracts International, Volume: 80-01(E), Section: B.
Adviser: Alexei A. Efros. |
| 요약 | Deep networks are extremely adept at mapping a noisy, high-dimensional signal to a clean, low-dimensional target output (e.g., image classification). By solving this heavy compression task, the network also learns about natural image priors. How |
| 요약 | Part I describes the use of deep networks for conditional image synthesis. The section begins by exploring the problem of image colorization, proposing both automatic and user-guided approaches. This section then proposes a system for general im |
| 요약 | Part II explores the visual representations learned within deep networks. Colorization, as well as cross-channel prediction in general, is a simple but powerful pretext task for self-supervised learning. The representations from cross-channel pr |
| 일반주제명 | Artificial intelligence. Electrical engineering. Computer science. |
| 언어 | 영어 |
| 기본자료 저록 | Dissertation Abstracts International80-01B(E). Dissertation Abstract International |
| 대출바로가기 | http://www.riss.kr/pdu/ddodLink.do?id=T14998287 |
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| No. | 등록번호 | 청구기호 | 소장처 | 도서상태 | 반납예정일 | 예약 | 서비스 | 매체정보 |
|---|---|---|---|---|---|---|---|---|
| 1 | WE00027707 | 001.5 | 가야대학교/전자책서버(컴퓨터서버)/ | 대출가능 |
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