9783319863832 - domain adaptation in computer vision applications (advances in computer vision and pattern recognition) (2 Ergebnisse)
Sprache: Englisch
Verlag: Springer, 2018
Serie: Advances in Computer Vision and Pattern Recognition, Buch 65 von 86. Buch 65 von 86 - Advances in Computer Vision and Pattern Recognition
- Softcover
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EUR 140,10
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Taschenbuch. Zustand: Neu. Domain Adaptation in Computer Vision Applications | Gabriela Csurka | Taschenbuch | Advances in Computer Vision and Pattern Recognition | x | Englisch | 2018 | Springer | EAN 9783319863832 | Verantwortliche Person für die EU: Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg, juergen[dot]hartma…nn[at]springer[dot]com | Anbieter: preigu.
Sprache: Englisch
Verlag: Springer, 2018
Serie: Advances in Computer Vision and Pattern Recognition, Buch 65 von 86. Buch 65 von 86 - Advances in Computer Vision and Pattern Recognition
- Softcover
Anbieter: AHA-BUCH GmbH, Einbeck, DeutschlandAHA-BUCH GmbH
Verkäufer/-in kontaktierenVerkäufer/-in mit 5 SternenZustand: Neu
EUR 166,62
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Taschenbuch. Zustand: Neu. Druck auf Anfrage Neuware - Printed after ordering - This comprehensive text/reference presents a broad review of diverse domain adaptation (DA) methods for machine learning, with a focus on solutions for visual applications. The book collects together solutions and perspectives proposed by an internat…ional selection of pre-eminent experts in the field, addressing not only classical image categorization, but also other computer vision tasks such as detection, segmentation and visual attributes.Topics and features: surveys the complete field of visual DA, including shallow methods designed for homogeneous and heterogeneous data as well as deep architectures; presents a positioning of the dataset bias in the CNN-based feature arena; proposes detailed analyses of popular shallow methods that addresses landmark data selection, kernel embedding, feature alignment, joint feature transformation and classifier adaptation, or the case of limited access to the source data; discusses more recent deep DA methods, including discrepancy-based adaptation networks and adversarial discriminative DA models; addresses domain adaptation problems beyond image categorization, such as a Fisher encoding adaptation for vehicle re-identification, semantic segmentation and detection trained on synthetic images, and domain generalization for semantic part detection; describes a multi-source domain generalization technique for visual attributes and a unifying framework for multi-domain and multi-task learning.This authoritative volume will be of great interest to a broad audience ranging from researchers and practitioners, to students involved in computer vision, pattern recognition and machine learning.
