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001 978-3-031-79175-8
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020 _a9783031791758
_9978-3-031-79175-8
024 7 _a10.1007/978-3-031-79175-8
_2doi
050 4 _aTA1501-1820
050 4 _aTA1634
072 7 _aUYT
_2bicssc
072 7 _aCOM016000
_2bisacsh
072 7 _aUYT
_2thema
082 0 4 _a006
_223
100 1 _aCsurka, Gabriela.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
_983827
245 1 0 _aVisual Domain Adaptation in the Deep Learning Era
_h[electronic resource] /
_cby Gabriela Csurka, Timothy M. Hospedales, Mathieu Salzmann, Tatiana Tommasi.
250 _a1st ed. 2022.
264 1 _aCham :
_bSpringer International Publishing :
_bImprint: Springer,
_c2022.
300 _aIV, 190 p.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
347 _atext file
_bPDF
_2rda
490 1 _aSynthesis Lectures on Computer Vision,
_x2153-1064
520 _aSolving 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 domain adaptation problem, we then explore the rich space of problem settings that arise when applying domain adaptation in practice such as partial or open-set DA, where source and target data categories do not fully overlap, continuous DA where the target data comes as a stream, and so on. We next consider the least restrictive setting of domain generalization (DG), as an extreme case where neither labeled nor unlabeled target data are available during training. Finally, we close by considering the emerging area of learning-to-learn and how it can be applied to further improve existing approaches to cross domain learning problems such as DA and DG.
650 0 _aImage processing
_xDigital techniques.
_94145
650 0 _aComputer vision.
_983828
650 0 _aPattern recognition systems.
_93953
650 1 4 _aComputer Imaging, Vision, Pattern Recognition and Graphics.
_931569
650 2 4 _aComputer Vision.
_983830
650 2 4 _aAutomated Pattern Recognition.
_931568
700 1 _aHospedales, Timothy M.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
_983832
700 1 _aSalzmann, Mathieu.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
_983833
700 1 _aTommasi, Tatiana.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
_983834
710 2 _aSpringerLink (Online service)
_983839
773 0 _tSpringer Nature eBook
776 0 8 _iPrinted edition:
_z9783031791802
776 0 8 _iPrinted edition:
_z9783031791703
776 0 8 _iPrinted edition:
_z9783031791857
830 0 _aSynthesis Lectures on Computer Vision,
_x2153-1064
_983840
856 4 0 _uhttps://doi.org/10.1007/978-3-031-79175-8
912 _aZDB-2-SXSC
942 _cEBK
999 _c85566
_d85566