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An Exploratory Journey of Representation Learning's Enhancement, Adaptation and Related Intelligent Methods
An Exploratory Journey of Representation Learning's Enhancement, Adaptation and Related Intelligent Methods
상세정보
- 자료유형
- 학위논문 서양
- 최종처리일시
- 20260202105659
- ISBN
- 9798263307622
- DDC
- 630
- 저자명
- Wu, Jing.
- 서명/저자
- An Exploratory Journey of Representation Learnings Enhancement, Adaptation and Related Intelligent Methods
- 발행사항
- [Sl] : University of Illinois at Urbana-Champaign, 2024
- 발행사항
- Ann Arbor : ProQuest Dissertations & Theses, 2024
- 형태사항
- 159 p
- 주기사항
- Source: Dissertations Abstracts International, Volume: 87-05, Section: B.
- 주기사항
- Advisor: Hovakimyan, Naira.
- 학위논문주기
- Thesis (Ph.D.)--University of Illinois at Urbana-Champaign, 2024.
- 초록/해제
- 요약Representation learning models employing Siamese structures have consistently demonstrated exceptional performance across various fields, including deep learning, computer vision, and natural language processing. Furthermore, the applicability of representation learning has broadened to encompass wider domains such as agriculture, remote sensing, and earth observation, which are significantly challenged by data scarcity. This dissertation aims to enhance the quality and adaptability of learned representations across these diverse application domains. Meanwhile, we have also expanded the scope of our research to a broader area of intelligent agricultural systems.Initially, we delve into contrastive representation learning within the general computer vision domain and introduce a novel "Hallucinator" module to reduce mutual information, increase the batch size of positive pairs, and improve representation quality. Subsequently, we extend the representation framework to agriculture and remote sensing, proposing spatial-temporal-aware architectures tailored to the unique characteristics of remote sensing data. Furthermore, we introduce the Extended Agriculture Vision dataset to address data scarcity issues and showcase the effectiveness of proposed representation frameworks.Furthermore, we demonstrate that the learned representations are powerful features for few-shot tasks in remote sensing and earth observation. We introduce GenCo, a generator-based representation learning framework that simultaneously pre-trains backbones and explores variants of feature samples. During fine-tuning, the auxiliary generator enriches the limited labeled data samples in the feature space. We validate the effectiveness of our method in enhancing few-shot learning performance on the Agriculture-Vision and EuroSAT datasets. Notably, our few-shot approach surpasses purely supervised training in both classification and semantic segmentation tasks trained over ten thousand images in the Agriculture-Vision Dataset.Lastly, we propose an intelligent nitrogen (N) management system utilizing deep reinforcement learning (RL) in conjunction with crop simulations through the Decision Support System for Agrotechnology Transfer (DSSAT). Initially, we framed the N management issue as an RL problem. Subsequently, we train management policies using deep Q-network and soft actor-critic algorithms, along with the Gym-DSSAT interface. This interface facilitates daily interactions between the simulated crop environment and RL agents. According to our experiments with maize crops in both Iowa and Florida, USA, the RL-trained policies surpass previous empirical methods.
- 일반주제명
- Agriculture
- 일반주제명
- Agricultural engineering
- 키워드
- Machine learning
- 기타저자
- University of Illinois at Urbana-Champaign Mechanical Sci & Engineering
- 기본자료저록
- Dissertations Abstracts International. 87-05B.
- 전자적 위치 및 접속
- 로그인 후 원문을 볼 수 있습니다.
MARC
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■020 ▼a9798263307622
■035 ▼a(MiAaPQ)AAI32409844
■035 ▼a(MiAaPQ)124344
■040 ▼aMiAaPQ▼cMiAaPQ
■0820 ▼a630
■1001 ▼aWu, Jing.
■24513▼aAn Exploratory Journey of Representation Learning's Enhancement, Adaptation and Related Intelligent Methods
■260 ▼a[Sl]▼bUniversity of Illinois at Urbana-Champaign▼c2024
■260 1▼aAnn Arbor▼bProQuest Dissertations & Theses▼c2024
■300 ▼a159 p
■500 ▼aSource: Dissertations Abstracts International, Volume: 87-05, Section: B.
■500 ▼aAdvisor: Hovakimyan, Naira.
■5021 ▼aThesis (Ph.D.)--University of Illinois at Urbana-Champaign, 2024.
■520 ▼aRepresentation learning models employing Siamese structures have consistently demonstrated exceptional performance across various fields, including deep learning, computer vision, and natural language processing. Furthermore, the applicability of representation learning has broadened to encompass wider domains such as agriculture, remote sensing, and earth observation, which are significantly challenged by data scarcity. This dissertation aims to enhance the quality and adaptability of learned representations across these diverse application domains. Meanwhile, we have also expanded the scope of our research to a broader area of intelligent agricultural systems.Initially, we delve into contrastive representation learning within the general computer vision domain and introduce a novel "Hallucinator" module to reduce mutual information, increase the batch size of positive pairs, and improve representation quality. Subsequently, we extend the representation framework to agriculture and remote sensing, proposing spatial-temporal-aware architectures tailored to the unique characteristics of remote sensing data. Furthermore, we introduce the Extended Agriculture Vision dataset to address data scarcity issues and showcase the effectiveness of proposed representation frameworks.Furthermore, we demonstrate that the learned representations are powerful features for few-shot tasks in remote sensing and earth observation. We introduce GenCo, a generator-based representation learning framework that simultaneously pre-trains backbones and explores variants of feature samples. During fine-tuning, the auxiliary generator enriches the limited labeled data samples in the feature space. We validate the effectiveness of our method in enhancing few-shot learning performance on the Agriculture-Vision and EuroSAT datasets. Notably, our few-shot approach surpasses purely supervised training in both classification and semantic segmentation tasks trained over ten thousand images in the Agriculture-Vision Dataset.Lastly, we propose an intelligent nitrogen (N) management system utilizing deep reinforcement learning (RL) in conjunction with crop simulations through the Decision Support System for Agrotechnology Transfer (DSSAT). Initially, we framed the N management issue as an RL problem. Subsequently, we train management policies using deep Q-network and soft actor-critic algorithms, along with the Gym-DSSAT interface. This interface facilitates daily interactions between the simulated crop environment and RL agents. According to our experiments with maize crops in both Iowa and Florida, USA, the RL-trained policies surpass previous empirical methods.
■590 ▼aSchool code: 0090.
■650 4▼aAgriculture
■650 4▼aAgricultural engineering
■653 ▼aRepresentation learning
■653 ▼aMachine learning
■653 ▼aIntelligent agriculture
■653 ▼aNatural language processing
■690 ▼a0800
■690 ▼a0473
■690 ▼a0539
■71020▼aUniversity of Illinois at Urbana-Champaign▼bMechanical Sci & Engineering.
■7730 ▼tDissertations Abstracts International▼g87-05B.
■790 ▼a0090
■791 ▼aPh.D.
■792 ▼a2024
■793 ▼aEnglish
■85640▼uhttp://www.riss.kr/pdu/ddodLink.do?id=T17361059▼nKERIS▼z이 자료의 원문은 한국교육학술정보원에서 제공합니다.
