| LDR | | 02252nam u200445 4500 |
| 001 | | 000000421600 |
| 005 | | 20190215165440 |
| 008 | | 181129s2018 |||||||||||||||||c||eng d |
| 020 | |
▼a 9780438324800 |
| 035 | |
▼a (MiAaPQ)AAI10816767 |
| 035 | |
▼a (MiAaPQ)berkeley:17873 |
| 040 | |
▼a MiAaPQ
▼c MiAaPQ
▼d 247004 |
| 082 | 0 |
▼a 001.5 |
| 100 | 1 |
▼a Zhang, Richard. |
| 245 | 10 |
▼a Image Synthesis for Self-supervised Visual Representation Learning. |
| 260 | |
▼a [S.l.]:
▼b University of California, Berkeley.,
▼c 2018. |
| 260 | 1 |
▼a Ann Arbor:
▼b ProQuest Dissertations & Theses,
▼c 2018. |
| 300 | |
▼a 138 p. |
| 500 | |
▼a Source: Dissertation Abstracts International, Volume: 80-01(E), Section: B. |
| 500 | |
▼a Adviser: Alexei A. Efros. |
| 502 | 1 |
▼a Thesis (Ph.D.)--University of California, Berkeley, 2018. |
| 520 | |
▼a 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 |
| 520 | |
▼a 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 |
| 520 | |
▼a 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 |
| 590 | |
▼a School code: 0028. |
| 650 | 4 |
▼a Artificial intelligence. |
| 650 | 4 |
▼a Electrical engineering. |
| 650 | 4 |
▼a Computer science. |
| 690 | |
▼a 0800 |
| 690 | |
▼a 0544 |
| 690 | |
▼a 0984 |
| 710 | 20 |
▼a University of California, Berkeley.
▼b Electrical Engineering & Computer Sciences. |
| 773 | 0 |
▼t Dissertation Abstracts International
▼g 80-01B(E). |
| 773 | |
▼t Dissertation Abstract International |
| 790 | |
▼a 0028 |
| 791 | |
▼a Ph.D. |
| 792 | |
▼a 2018 |
| 793 | |
▼a English |
| 856 | 40 |
▼u http://www.riss.kr/pdu/ddodLink.do?id=T14998287
▼n KERIS
▼z 이 자료의 원문은 한국교육학술정보원에서 제공합니다. |
| 980 | |
▼a 201812
▼f 2019 |
| 990 | |
▼a ***1012033 |