رسالة جامعية
Practical Robust Learning Under Domain Shifts
| العنوان: | Practical Robust Learning Under Domain Shifts |
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| المؤلفون: | Yang, Luyu |
| المساهمون: | Shrivastava, Abhinav AS, Davis, Larry LD, Computer Science, Digital Repository at the University of Maryland, University of Maryland (College Park, Md.) |
| سنة النشر: | 2022 |
| المجموعة: | University of Maryland: Digital Repository (DRUM) |
| مصطلحات موضوعية: | Computer science, distribution shift, domain adaptation, online learning, robust learning, semi-supervised learning, unsupervised learning |
| الوصف: | With the constantly upgraded devices, the data we capture is shifting with time. Despite the domain shifts among the images, we as humans can put aside the difference and still recognize the content. However, these shifts are a bigger challenge for machines. It is widely known that humans are naturally adaptive to the visual changes in the environment, without learning all over again. However, to make machines work in the changed environment we need new annotations from human. The fundamental question is: can we make machines as adaptive as humans? In this thesis, we have worked towards addressing this question through advances in the study of robust learning under domain shifts via domain adaptation. Our goal is to facilitate the transfer of information of the machines while minimizing the need for human supervision. To enable real systems with demonstrated robustness, the study of domain adaptation needs to move from ideals to realities. In current domain adaptation research, there are few ideals that are not consistent with reality: i) The assumption that domains are perfectly sliced and that domain labels are available. ii) The assumption that the annotations from the target domain should be treated equally as those of the source domain. iii) The assumption that the samples of target domains are constantly accessible. In this thesis, we try to address the issue that true domain labels are hard to obtain, the target domain labels have better ways to exploited, and that in reality the target domain is often time-sensitive. In the scope of problem settings, this thesis has covered the following scenarios with practical values. Unsupervised multi-source domain adaptation, semi-supervised domain adaptation and online domain adaptation. Three completed works are reviewed corresponding to each problem setting. The first work proposes an adversarial learning strategy that learns a dynamic curriculum for source samples to maximize the utility of source labels of multiple domains. The model iteratively learns which ... |
| نوع الوثيقة: | doctoral or postdoctoral thesis |
| وصف الملف: | application/pdf |
| اللغة: | English |
| العلاقة: | https://doi.org/10.13016/vul8-xdbkTest; http://hdl.handle.net/1903/29541Test |
| DOI: | 10.13016/vul8-xdbk |
| الإتاحة: | https://doi.org/10.13016/vul8-xdbkTest http://hdl.handle.net/1903/29541Test |
| رقم الانضمام: | edsbas.BB2B8BAF |
| قاعدة البيانات: | BASE |
| DOI: | 10.13016/vul8-xdbk |
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