| LDR | | 02449nam u200433 4500 |
| 001 | | 000000418673 |
| 005 | | 20190215163045 |
| 008 | | 181129s2018 |||||||||||||||||c||eng d |
| 020 | |
▼a 9780438402164 |
| 035 | |
▼a (MiAaPQ)AAI10845158 |
| 035 | |
▼a (MiAaPQ)umd:19353 |
| 040 | |
▼a MiAaPQ
▼c MiAaPQ
▼d 247004 |
| 082 | 0 |
▼a 004 |
| 100 | 1 |
▼a Santhanam, Venkataraman.
▼0 (orcid)0000-0002-2134-4035. |
| 245 | 10 |
▼a Towards Generalized Frameworks for Object Recognition. |
| 260 | |
▼a [S.l.]:
▼b University of Maryland, College Park.,
▼c 2018. |
| 260 | 1 |
▼a Ann Arbor:
▼b ProQuest Dissertations & Theses,
▼c 2018. |
| 300 | |
▼a 117 p. |
| 500 | |
▼a Source: Dissertation Abstracts International, Volume: 80-02(E), Section: B. |
| 500 | |
▼a Adviser: Larry S. Davis. |
| 502 | 1 |
▼a Thesis (Ph.D.)--University of Maryland, College Park, 2018. |
| 520 | |
▼a Over the past few years, deep convolutional neural network (DCNN) based approaches have been immensely successful in tackling a diverse range of object recognition problems. Popular DCNN architectures like deep residual networks (ResNets) are hi |
| 520 | |
▼a We first present a generic DCNN architecture for Im2Im regression that can be trained end-to-end without any further machinery. Our proposed architecture, the Recursively Branched Deconvolutional Network (RBDN), which features a cheap early mult |
| 520 | |
▼a Second, we focus on gradient flow and optimization in ResNets. In particular, we theoretically analyze why pre-activation(v2) ResNets outperform the original ResNets(v1) on CIFAR datasets but not on ImageNet. Our analysis reveals that although v |
| 520 | |
▼a Finally, we present a robust non-parametric probabilistic ensemble method for multi-classification, which outperforms the state-of-the-art ensemble methods on several machine learning and computer vision datasets for object recognition with st |
| 590 | |
▼a School code: 0117. |
| 650 | 4 |
▼a Computer science. |
| 650 | 4 |
▼a Artificial intelligence. |
| 690 | |
▼a 0984 |
| 690 | |
▼a 0800 |
| 710 | 20 |
▼a University of Maryland, College Park.
▼b Computer Science. |
| 773 | 0 |
▼t Dissertation Abstracts International
▼g 80-02B(E). |
| 773 | |
▼t Dissertation Abstract International |
| 790 | |
▼a 0117 |
| 791 | |
▼a Ph.D. |
| 792 | |
▼a 2018 |
| 793 | |
▼a English |
| 856 | 40 |
▼u http://www.riss.kr/pdu/ddodLink.do?id=T15000044
▼n KERIS
▼z 이 자료의 원문은 한국교육학술정보원에서 제공합니다. |
| 980 | |
▼a 201812
▼f 2019 |
| 990 | |
▼a ***1012033 |