Unsupervised Domain Adaptation: Recent Advances and Future Perspectives

Li, Jingjing, Zhu, Lei, Du, Zhekai

Unsupervised Domain Adaptation: Recent Advances and Future Perspectives
  • 出版商: Springer
  • 出版日期: 2024-04-23
  • 售價: $7,750
  • 貴賓價: 9.5 折 $7,363
  • 語言: 英文
  • 頁數: 223
  • 裝訂: Hardcover - also called cloth, retail trade, or trade
  • ISBN: 9819710243
  • ISBN-13: 9789819710249

    • 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（UDA）是機器學習中一個具有挑戰性的問題，該模型在帶有標記數據的源領域上進行訓練，並在沒有標記數據的目標領域上進行測試。近年來，UDA在研究界引起了廣泛關注，因為它在各種實際應用場景中具有潛力。本書全面回顧了最新的UDA方法，並探索了有潛力推動該領域發展的新變體。

本書首先清晰介紹了UDA問題，主要分為四個技術部分，每個部分都專注於UDA研究的特定方面。第一部分介紹了基於準則優化的UDA，旨在通過最小化源領域和目標領域之間的差異來學習領域不變表示。第二部分討論了基於雙分類器對抗學習的UDA，通過在特徵提取器和兩個任務分類器之間進行極小極大博弈，創造性地利用對抗學習。第三部分介紹了無源UDA，這是一種新的UDA設置，不需要來自源領域的任何原始數據。第四部分介紹了UDA的主動學習，將領域適應和主動學習結合起來，以減少適應所需的標記數據量。

本書適合對UDA及其在各個領域中的應用感興趣的研究人員、研究生和從業人員，主要是在計算機視覺領域。各章節由該領域的領先專家撰寫，提供了對當前UDA方法的全面深入分析，並提出了未來研究的新方向。本書以其廣泛的涵蓋範圍和尖端研究成果，是任何希望提升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)

Jingjing Li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。他分別於2010年、2013年和2017年在UESTC獲得了學士、碩士和博士學位。他的研究興趣主要集中在領域適應和零樣本學習領域。他已經在IEEE TPAMI、IEEE TIP、IEEE TKDE、CVPR、ICCV、AAAI、IJCAI和ACM Multimedia等期刊和會議上合著/撰寫了70多篇同行評審的論文。他於2018年獲得了中國電子學會優秀博士論文獎。

Lei Zhu目前是同濟大學電子與信息工程學院的教授。他分別於2009年和2015年在武漢理工大學和華中科技大學獲得了學士和博士學位。他曾在昆士蘭大學擔任研究員（2016-2017年）。他的研究興趣主要集中在大規模多媒體內容分析和檢索領域。Zhu已經在ACM SIGIR、ACM MM、IEEE TPAMI、IEEE TIP、IEEE TKDE和ACM TOIS等期刊和會議上合著/撰寫了100多篇同行評審的論文。他的出版物已經引起了超過5,600次的Google引用。目前，他擔任IEEE TBD、ACM TOMM和Information Sciences的副編輯。他曾擔任40多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、ChinaMM 2022最佳學生論文獎、ACM China SIGMM新星獎、山東省留學人員創業獎和山東省人工智能優秀青年獎。

Zhekai Du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。他的研究興趣主要集中在領域適應、領域泛化及其在計算機視覺中的應用。他於2018年在UESTC獲得了學士學位。他已經在CVPR、ACM Multimedia、ECCV、AAAI和IEEE TPAMI等頂級會議和期刊上合著/撰寫了數十篇論文。

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