Domain Adaptation in Computer Vision Applications

Name: Domain Adaptation in Computer Vision Applications
Brand: Springer International Publishing AG
SKU: 9783319583464
Price: 157.83 EUR
Availability: InStock

Автор: Gabriela Csurka

Език:

Корици: С твърди корици

Издател: Springer International Publishing AG

Наличност: Външен склад

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157.83 € 308.69 лв

This comprehensive text/reference presents a broad review of diverse domain adaptation (DA) methods...

Информация за книгата

Автор

Gabriela Csurka

Език

Английски език

Корици

Книга - С твърди корици

Издадена

2017

страници

344

EAN

9783319583464

ISBN

3319583468

Enbook ID

16190120

Издател

Springer International Publishing AG

Теглоt

7209

Размери

155 x 235 x 22

Пълно описание

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 international 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.

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Domain Adaptation in Computer Vision Applications

Информация за книгата

Пълно описание

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