Domain Adaptation and Representation Transfer, Kamnitsas
Автор: Hamilton, William L. Название: Graph Representation Learning ISBN: 3031004604 ISBN-13(EAN): 9783031004605 Издательство: Springer Рейтинг: Цена: 51230.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: These advances in graph representation learning have led to new state-of-the-art results in numerous domains, including chemical synthesis, 3D vision, recommender systems, question answering, and social network analysis.This book provides a synthesis and overview of graph representation learning.
Автор: Qian Wang; Fausto Milletari; Hien V. Nguyen; Shadi Название: Domain Adaptation and Representation Transfer and Medical Image Learning with Less Labels and Imperfect Data ISBN: 3030333906 ISBN-13(EAN): 9783030333904 Издательство: Springer Рейтинг: Цена: 46570.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: This book constitutes the refereed proceedings of the First MICCAI Workshop on Domain Adaptation and Representation Transfer, DART 2019, and the First International Workshop on Medical Image Learning with Less Labels and Imperfect Data, MIL3ID 2019, held in conjunction with MICCAI 2019, in Shenzhen, China, in October 2019.
Автор: Albarqouni Shadi, Bakas Spyridon, Kamnitsas Konstantinos Название: Domain Adaptation and Representation Transfer, and Distributed and Collaborative Learning: Second Miccai Workshop, Dart 2020, and First Miccai Worksho ISBN: 3030605477 ISBN-13(EAN): 9783030605476 Издательство: Springer Рейтинг: Цена: 46570.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: This book constitutes the refereed proceedings of the Second MICCAI Workshop on Domain Adaptation and Representation Transfer, DART 2020, and the First MICCAI Workshop on Distributed and Collaborative Learning, DCL 2020, held in conjunction with MICCAI 2020 in October 2020.
Автор: Albarqouni Shadi, Cardoso M. Jorge, Dou Qi Название: Domain Adaptation and Representation Transfer, and Affordable Healthcare and AI for Resource Diverse Global Health: Third MICCAI Workshop, DART 2021, ISBN: 3030877213 ISBN-13(EAN): 9783030877217 Издательство: Springer Рейтинг: Цена: 60550.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: This book constitutes the refereed proceedings of the Third MICCAI Workshop on Domain Adaptation and Representation Transfer, DART 2021, and the First MICCAI Workshop on Affordable Healthcare and AI for Resource Diverse Global Health, FAIR 2021, held in conjunction with MICCAI 2021, in September/October 2021.
Автор: Levgen, Redko Название: Domain Adaptation Theory ISBN: 178548236X ISBN-13(EAN): 9781785482366 Издательство: Elsevier Science Рейтинг: Цена: 102180.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание:
Domain Adaptation Theory: Available Theoretical Results gives the current state-of-the-art results on transfer learning, with a particular focus on domain adaptation from a theoretical point-of-view. The book begins with a brief overview of the most popular concepts used to provide generalization guarantees, and includes sections on Vapnik-Chervonenkis (VC), Rademacher, PAC-Bayesian, Robustness and Stability based bounds. In addition, the book explains the domain adaptation problem and describes the four major families of theoretical results that exist in the literature, including the Divergence based bounds. Next, PAC-Bayesian bounds are discussed, including the original PAC-Bayesian bounds for domain adaptation and their updated version.
This part of the book is followed by two sections presenting generalization guarantees based on the robustness and stability properties of the learning algorithm.
Gives an overview of current results on transfer learning
Focuses on the adaptation of the field from a theoretical point-of-view
Describes four major families of theoretical results in the literature
Summarizes the main existing results in the field of adaptation of the field
Provides tips for future research
Автор: Gopalan Raghuraman, Li Ruonan, Patel Vishal M. Название: Domain Adaptation for Visual Recognition ISBN: 1680830309 ISBN-13(EAN): 9781680830309 Издательство: Неизвестно Цена: 68970.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: This book provides a comprehensive overview of domain adaptation solutions for visual recognition problems. It discusses three adaptation scenarios namely, (i) unsupervised adaptation; (ii) semi-supervised adaptation and (iii) multi-domain heterogeneous adaptation.
Автор: Richa Singh; Mayank Vatsa; Vishal M Patel; Nalini Название: Domain Adaptation for Visual Understanding ISBN: 3030306704 ISBN-13(EAN): 9783030306700 Издательство: Springer Рейтинг: Цена: 93160.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: This unique volume reviews the latest advances in domain adaptation in the training of machine learning algorithms for visual understanding, offering valuable insights from an international selection of experts in the field.
Автор: Csurka Gabriela Название: Domain Adaptation in Computer Vision Applications ISBN: 3319863835 ISBN-13(EAN): 9783319863832 Издательство: Springer Рейтинг: Цена: 139750.00 T Наличие на складе: Поставка под заказ. 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.
Автор: Venkateswara Hemanth, Panchanathan Sethuraman Название: Domain Adaptation in Computer Vision with Deep Learning ISBN: 3030455289 ISBN-13(EAN): 9783030455286 Издательство: Springer Рейтинг: Цена: 139750.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: Preface.- Part I: Introduction.- Chapter 1: Introduction to Domain Adaptation.- Chapter 2: Shallow Domain Adaptation.- Part II: Domain Alignment in the Feature Space.- Chapter 3: d-SNE: Domain Adaptation using Stochastic Neighborhood Embedding.- Chapter 4: Deep Hashing Network for Unsupervised Domain Adaptation.- Chapter 5: Re-weighted Adversarial Adaptation Network for Unsupervised Domain Adaptation.- Part III: Domain Alignment in the Image Space.- Chapter 6: Unsupervised Domain Adaptation with Duplex Generative Adversarial Network.- Chapter 7: Domain Adaptation via Image to Image Translation.- Chapter 8: Domain Adaptation via Image Style Transfer.- Part IV: Future Directions in Domain Adaptation.- Chapter 9: Towards Scalable Image Classifier Learning with Noisy Labels via Domain Adaptation.- Chapter 10: Adversarial Learning Approach for Open Set Domain Adaptation.- Chapter 11: Universal Domain Adaptation.- Chapter 12: Multi-source Domain Adaptation by Deep CockTail Networks.- Chapter 13: Zero-Shot Task Transfer.
Автор: Singh Richa, Vatsa Mayank, Patel Vishal M. Название: Domain Adaptation for Visual Understanding ISBN: 3030306739 ISBN-13(EAN): 9783030306731 Издательство: Springer Цена: 93160.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: This unique volume reviews the latest advances in domain adaptation in the training of machine learning algorithms for visual understanding, offering valuable insights from an international selection of experts in the field.
Автор: Venkateswara Hemanth, Panchanathan Sethuraman Название: Domain Adaptation in Computer Vision with Deep Learning ISBN: 3030455319 ISBN-13(EAN): 9783030455316 Издательство: Springer Цена: 139750.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание:
This book provides a survey of deep learning approaches to domain adaptation in computer vision. It gives the reader an overview of the state-of-the-art research in deep learning based domain adaptation. This book also discusses the various approaches to deep learning based domain adaptation in recent years. It outlines the importance of domain adaptation for the advancement of computer vision, consolidates the research in the area and provides the reader with promising directions for future research in domain adaptation.
Divided into four parts, the first part of this book begins with an introduction to domain adaptation, which outlines the problem statement, the role of domain adaptation and the motivation for research in this area. It includes a chapter outlining pre-deep learning era domain adaptation techniques. The second part of this book highlights feature alignment based approaches to domain adaptation. The third part of this book outlines image alignment procedures for domain adaptation. The final section of this book presents novel directions for research in domain adaptation.
This book targets researchers working in artificial intelligence, machine learning, deep learning and computer vision. Industry professionals and entrepreneurs seeking to adopt deep learning into their applications will also be interested in this book.
Автор: Csurka Название: Visual Domain Adaptation in the Deep Learning Era ISBN: 3031791703 ISBN-13(EAN): 9783031791703 Издательство: Springer Рейтинг: Цена: 46570.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: 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.
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