Visual Domain Adaptation in the Deep Learning Era, Csurka

Автор: Arindam Chaudhuri
Название: Visual and Text Sentiment Analysis through Hierarchical Deep Learning Networks
ISBN: 9811374732 ISBN-13(EAN): 9789811374739
Издательство: Springer
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Цена: 46570.00 T
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Описание: This book presents the latest research on hierarchical deep learning for multi-modal sentiment analysis. Further, it analyses sentiments in Twitter blogs from both textual and visual content using hierarchical deep learning networks: hierarchical gated feedback recurrent neural networks (HGFRNNs). Several studies on deep learning have been conducted to date, but most of the current methods focus on either only textual content, or only visual content. In contrast, the proposed sentiment analysis model can be applied to any social blog dataset, making the book highly beneficial for postgraduate students and researchers in deep learning and sentiment analysis. The mathematical abstraction of the sentiment analysis model is presented in a very lucid manner. The complete sentiments are analysed by combining text and visual prediction results. The book’s novelty lies in its development of innovative hierarchical recurrent neural networks for analysing sentiments; stacking of multiple recurrent layers by controlling the signal flow from upper recurrent layers to lower layers through a global gating unit; evaluation of HGFRNNs with different types of recurrent units; and adaptive assignment of HGFRNN layers to different timescales. Considering the need to leverage large-scale social multimedia content for sentiment analysis, both state-of-the-art visual and textual sentiment analysis techniques are used for joint visual-textual sentiment analysis. The proposed method yields promising results from Twitter datasets that include both texts and images, which support the theoretical hypothesis.

Автор: Csurka Gabriela, Hospedales Timothy M., Salzmann Mathieu
Название: Visual Domain Adaptation in the Deep Learning Era
ISBN: 1636393411 ISBN-13(EAN): 9781636393414
Издательство: Mare Nostrum (Eurospan)
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Цена: 74850.00 T
Наличие на складе: Нет в наличии.
Описание: The aim of this book is to provide an overview of such domain adaptation /transfer learning methods applied to computer vision, a field whose popularity has increased significantly in the last few years.

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: 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
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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
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Цена: 93160.00 T
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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: 3030455289 ISBN-13(EAN): 9783030455286
Издательство: Springer
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Цена: 139750.00 T
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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: 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: 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
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Цена: 46570.00 T
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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
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Цена: 60550.00 T
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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: 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
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Цена: 46570.00 T
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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: Kamnitsas
Название: Domain Adaptation and Representation Transfer
ISBN: 3031168518 ISBN-13(EAN): 9783031168512
Издательство: Springer
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Цена: 51230.00 T
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Описание: This book constitutes the refereed proceedings of the 4th MICCAI Workshop on Domain Adaptation and Representation Transfer, DART 2022, held in conjunction with MICCAI 2022, in September 2022. DART 2022 accepted 13 papers from the 25 submissions received. The workshop aims at creating a discussion forum to compare, evaluate, and discuss methodological advancements and ideas that can improve the applicability of machine learning (ML)/deep learning (DL) approaches to clinical setting by making them robust and consistent across different domains.

Автор: Csurka Gabriela
Название: Domain Adaptation in Computer Vision Applications
ISBN: 3319863835 ISBN-13(EAN): 9783319863832
Издательство: Springer
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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.