Unsupervised Domain Adaptation: Recent Advances and Future Perspectives
暫譯: 無監督領域適應：近期進展與未來展望

Li, Jingjing, Zhu, Lei, Du, Zhekai

Unsupervised Domain Adaptation: Recent Advances and Future Perspectives
  • 出版商: Springer
  • 出版日期: 2025-04-23
  • 售價: $6,480
  • 貴賓價: 9.5 折 $6,156
  • 語言: 英文
  • 頁數: 223
  • 裝訂: Quality Paper - also called trade paper
  • ISBN: 9819710278
  • ISBN-13: 9789819710270

    • 海外代購書籍(需單獨結帳)

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商品描述

Unsupervised domain adaptation (UDA) is a challenging problem in machine learning where the model is trained on a source domain with labeled data and tested on a target domain with unlabeled data. In recent years, UDA has received significant attention from the research community due to its applicability in various real-world scenarios. This book provides a comprehensive review of state-of-the-art UDA methods and explores new variants of UDA that have the potential to advance the field.

The book begins with a clear introduction to the UDA problem and is mainly organized into four technical sections, each focused on a specific piece of UDA research. The first section covers criterion optimization-based UDA, which aims to learn domain-invariant representations by minimizing the discrepancy between source and target domains. The second section discusses bi-classifier adversarial learning-based UDA, which creatively leverages adversarial learning by conducting a minimax game between the feature extractor and two task classifiers. The third section introduces source-free UDA, a novel UDA setting that does not require any raw data from the source domain. The fourth section presents active learning for UDA, which combines domain adaptation and active learning to reduce the amount of labeled data needed for adaptation.

This book is suitable for researchers, graduate students, and practitioners who are interested in UDA and its applications in various fields, primarily in computer vision. The chapters are authored by leading experts in the field and provide a comprehensive and in-depth analysis of the current UDA methods and new directions for future research. With its broad coverage and cutting-edge research, this book is a valuable resource for anyone looking to advance their knowledge of UDA.


商品描述(中文翻譯)

無監督領域適應（Unsupervised Domain Adaptation, UDA）是機器學習中的一個挑戰性問題，模型在有標籤的來源領域上進行訓練，並在無標籤的目標領域上進行測試。近年來，由於其在各種現實世界場景中的應用，UDA受到了研究界的廣泛關注。本書提供了對最先進的UDA方法的全面回顧，並探討了具有推進該領域潛力的新變體。

本書以清晰的UDA問題介紹開始，主要分為四個技術部分，每個部分專注於特定的UDA研究。第一部分涵蓋基於準則優化的UDA，旨在通過最小化來源和目標領域之間的差異來學習領域不變的表示。第二部分討論基於雙分類器對抗學習的UDA，該方法創新性地利用對抗學習，通過在特徵提取器和兩個任務分類器之間進行最小最大博弈來實現。第三部分介紹無來源的UDA（source-free UDA），這是一種不需要任何來源領域原始數據的新型UDA設置。第四部分介紹了針對UDA的主動學習，該方法結合了領域適應和主動學習，以減少適應所需的標籤數據量。

本書適合對UDA及其在各個領域（主要是計算機視覺）應用感興趣的研究人員、研究生和實務工作者。各章節由該領域的領先專家撰寫，提供了對當前UDA方法的全面和深入分析，以及未來研究的新方向。憑藉其廣泛的覆蓋範圍和前沿的研究，本書是任何希望提升對UDA知識的人的寶貴資源。

作者簡介

Jingjing Li is currently a professor with the School of Computer Science and Engineering, University of Electronic Science and Technology of China (UESTC). He received his B.Eng., M.Sc. and Ph.D. degrees from UESTC in 2010, 2013, and 2017, respectively. His research interests are in the area of domain adaptation and zero-shot learning. He has co/authored more than 70 peer-reviewed papers, such as IEEE TPAMI, IEEE TIP, IEEE TKDE, CVPR, ICCV, AAAI, IJCAI, and ACM Multimedia. He won Excellent Doctoral Dissertation Award of Chinese Institute of Electronics in 2018.

Lei Zhu is currently a professor with the School of Electronic and Information Engineering, Tongji University. He received his B.Eng. and Ph.D. degrees from Wuhan University of Technology in 2009 and Huazhong University Science and Technology in 2015, respectively. He was a Research Fellow at the University of Queensland (2016-2017). His research interests are in the area of large-scale multimedia contentanalysis and retrieval. Zhu has co/authored more than 100 peer-reviewed papers, such as ACM SIGIR, ACM MM, IEEE TPAMI, IEEE TIP, IEEE TKDE, and ACM TOIS. His publications have attracted more than 5,600 Google citations. At present, he serves as the Associate Editor of IEEE TBD, ACM TOMM, and Information Sciences. He has served as the Area Chair, Senior Program Committee or reviewer for more than 40 well-known international journals and conferences. He won ACM SIGIR 2019 Best Paper Honorable Mention Award, ADMA 2020 Best Paper Award, ChinaMM 2022 Best Student Paper Award, ACM China SIGMM Rising Star Award, Shandong Provincial Entrepreneurship Award for Returned Students, and Shandong Provincial AI Outstanding Youth Award.

Zhekai Du is currently a third-year Ph.D. student with the School of Computer Science and Engineering, University of Electronic Science and Technology of China (UESTC). His research interests are domain adaptation, domain generalization, and their applications in computer vision. He received his B.Eng. degree from UESTC in 2018. He has co/authored dozens of papers at the top conferences and journals, like CVPR, ACM Multimedia, ECCV, AAAI, and IEEE TPAMI.

作者簡介(中文翻譯)

李晶晶目前是中國電子科技大學（UESTC）計算機科學與工程學院的教授。他於2010年、2013年和2017年分別獲得UESTC的工程學士、碩士和博士學位。他的研究興趣包括領域適應和零樣本學習。他已共同或獨立發表超過70篇經過同行評審的論文，發表於IEEE TPAMI、IEEE TIP、IEEE TKDE、CVPR、ICCV、AAAI、IJCAI和ACM Multimedia等期刊。他於2018年獲得中國電子學會優秀博士論文獎。

朱磊目前是同濟大學電子與信息工程學院的教授。他於2009年和2015年分別獲得武漢理工大學的工程學士學位和華中科技大學的博士學位。他曾於2016年至2017年擔任昆士蘭大學的研究員。他的研究興趣包括大規模多媒體內容分析和檢索。朱教授已共同或獨立發表超過100篇經過同行評審的論文，發表於ACM SIGIR、ACM MM、IEEE TPAMI、IEEE TIP、IEEE TKDE和ACM TOIS等期刊。他的出版物已獲得超過5,600次Google引用。目前，他擔任IEEE TBD、ACM TOMM和Information Sciences的副編輯。他曾擔任超過40個知名國際期刊和會議的區域主席、高級程序委員會成員或審稿人。他獲得了ACM SIGIR 2019最佳論文榮譽提名獎、ADMA 2020最佳論文獎、中國多媒體2022最佳學生論文獎、ACM中國SIGMM新星獎、山東省歸國學生創業獎和山東省人工智慧優秀青年獎。

杜哲凱目前是中國電子科技大學（UESTC）計算機科學與工程學院的三年級博士生。他的研究興趣包括領域適應、領域泛化及其在計算機視覺中的應用。他於2018年獲得UESTC的工程學士學位。他已在CVPR、ACM Multimedia、ECCV、AAAI和IEEE TPAMI等頂級會議和期刊上共同或獨立發表數十篇論文。

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