| 书目名称 | Visual Domain Adaptation in the Deep Learning Era | | 编辑 | Gabriela Csurka,Timothy M. Hospedales,Tatiana Tomm | | 视频video | | | 丛书名称 | Synthesis Lectures on Computer Vision | | 图书封面 |  | | 描述 | Solving problems with deep neural networks typically relies on massive amounts of labeled training data to achieve high performance. While in many situations huge volumes of unlabeled data can be and often are generated and available, the cost of acquiring data labels remains high. Transfer learning (TL), and in particular domain adaptation (DA), has emerged as an effective solution to overcome the burden of annotation, exploiting the unlabeled data available from the target domain together with labeled data or pre-trained models from similar, yet different source domains. The aim of this book is to provide an overview of such DA/TL methods applied to computer vision, a field whose popularity has increased significantly in the last few years. We set the stage by revisiting the theoretical background and some of the historical shallow methods before discussing and comparing different domain adaptation strategies that exploit deep architectures for visual recognition. We introduce the space of self-training-based methods that draw inspiration from the related fields of deep semi-supervised and self-supervised learning in solving the deep domain adaptation. Going beyond the classic do | | 出版日期 | Book 2022 | | 版次 | 1 | | doi | https://doi.org/10.1007/978-3-031-79175-8 | | isbn_softcover | 978-3-031-79170-3 | | isbn_ebook | 978-3-031-79175-8Series ISSN 2153-1056 Series E-ISSN 2153-1064 | | issn_series | 2153-1056 | | copyright | Springer Nature Switzerland AG 2022 |
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发表于 2025-3-21 19:18:19
