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Learning to Learn for Small Sample Visual Recognition

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자료유형E-Book
개인저자Wang, Yu-Xiong.
단체저자명Carnegie Mellon University.
서명/저자사항Learning to Learn for Small Sample Visual Recognition.
발행사항[S.l.] : Carnegie Mellon University., 2018
발행사항Ann Arbor : ProQuest Dissertations & Theses, 2018
형태사항206 p.
소장본 주기School code: 0041.
ISBN9780438257733
일반주기 Source: Dissertation Abstracts International, Volume: 79-12(E), Section: B.
Adviser: Martial Hebert.
요약Understanding how humans and machines recognize novel visual concepts from few examples remains a fundamental challenge. Humans are remarkably able to grasp a new concept and make meaningful generalization from just few examples. By contrast, st
요약This dissertation aims to endow visual recognition systems with low-shot learning ability, so that they learn consistently well on data of different sample sizes. Our key insight is that the visual world is well structured and highly predictable
요약We begin by learning from extremely limited data (e.g., one-shot learning). We cast the problem as supervised knowledge distillation and explore structures within model pairs. We introduce a meta-network that operates on the space of model param
요약To further decouple a recognition model from ties to a specific set of categories, we introduce self-supervision using meta-data. We expose the model to a large amount of unlabeled real-world images through an unsupervised meta-training phase. B
요약We them move on to learning from a medium sized number of examples and explore structures within an evolving model when learning from continuously changing data streams and tasks. We rethink the dominant knowledge transfer paradigm that fine-tun
요약From a different perspective, humans can imagine what novel objects look like from different views. Incorporating this ability to hallucinate novel instances of new concepts and leveraging joint structures in both data and task spaces might help
일반주제명Robotics.
Artificial intelligence.
Computer science.
언어영어
기본자료 저록Dissertation Abstracts International79-12B(E).
Dissertation Abstract International
대출바로가기http://www.riss.kr/pdu/ddodLink.do?id=T15001226

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