Adapting Learning Techniques For Visual Recognition

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Adapting Learning Techniques For Visual Recognition

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Author : Hao Zhang
language : en
Publisher:
Release Date : 2007

Adapting Learning Techniques For Visual Recognition written by Hao Zhang and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2007 with categories.

Visual Domain Adaptation In The Deep Learning Era

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Author : Gabriela Csurka
language : en
Publisher: Springer Nature
Release Date : 2022-06-06

Visual Domain Adaptation In The Deep Learning Era written by Gabriela Csurka and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-06-06 with Computers categories.

Solving 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.

Advanced Machine Learning Approaches For Brain Mapping

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Author : Dajiang Zhu
language : en
Publisher: Frontiers Media SA
Release Date : 2024-04-10

Advanced Machine Learning Approaches For Brain Mapping written by Dajiang Zhu and has been published by Frontiers Media SA this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-04-10 with Science categories.

Brain mapping is dedicated to using brain imaging techniques such as MRI, CT, PET, EEG, and fNIRS to understand the brain anatomy, structure, and function, and how it contributes to cognition, behavior, and deficits of brain diseases. Recently, machine learning is in a stage of rapid development, and various new technologies are continuously introduced into the field, from traditional approaches

Domain Adaptation In Computer Vision Applications

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Author : Gabriela Csurka
language : en
Publisher: Springer
Release Date : 2017-09-10

Domain Adaptation In Computer Vision Applications written by Gabriela Csurka and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2017-09-10 with Computers categories.

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.

Advanced Computational Intelligence Methods For Processing Brain Imaging Data

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Author : Kaijian Xia
language : en
Publisher: Frontiers Media SA
Release Date : 2022-11-09

Advanced Computational Intelligence Methods For Processing Brain Imaging Data written by Kaijian Xia and has been published by Frontiers Media SA this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-11-09 with Science categories.

Domain Adaptation And Representation Transfer And Affordable Healthcare And Ai For Resource Diverse Global Health

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Author : Shadi Albarqouni
language : en
Publisher: Springer Nature
Release Date : 2021-09-23

Domain Adaptation And Representation Transfer And Affordable Healthcare And Ai For Resource Diverse Global Health written by Shadi Albarqouni and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-09-23 with Computers categories.

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. The workshops were planned to take place in Strasbourg, France, but were held virtually due to the COVID-19 pandemic. DART 2021 accepted 13 papers from the 21 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. For FAIR 2021, 10 papers from 17 submissions were accepted for publication. They focus on Image-to-Image Translation particularly for low-dose or low-resolution settings; Model Compactness and Compression; Domain Adaptation and Transfer Learning; Active, Continual and Meta-Learning.

Domain Adaptation And Representation Transfer And Medical Image Learning With Less Labels And Imperfect Data

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Author : Qian Wang
language : en
Publisher: Springer Nature
Release Date : 2019-10-13

Domain Adaptation And Representation Transfer And Medical Image Learning With Less Labels And Imperfect Data written by Qian Wang and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-10-13 with Computers categories.

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. DART 2019 accepted 12 papers for publication out of 18 submissions. The papers deal with methodological advancements and ideas that can improve the applicability of machine learning and deep learning approaches to clinical settings by making them robust and consistent across different domains. MIL3ID accepted 16 papers out of 43 submissions for publication, dealing with best practices in medical image learning with label scarcity and data imperfection.

Domain Adaptation For Visual Understanding

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Author : Richa Singh
language : en
Publisher: Springer Nature
Release Date : 2020-01-08

Domain Adaptation For Visual Understanding written by Richa Singh and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-01-08 with Computers categories.

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. The text presents a diverse selection of novel techniques, covering applications of object recognition, face recognition, and action and event recognition. Topics and features: reviews the domain adaptation-based machine learning algorithms available for visual understanding, and provides a deep metric learning approach; introduces a novel unsupervised method for image-to-image translation, and a video segment retrieval model that utilizes ensemble learning; proposes a unique way to determine which dataset is most useful in the base training, in order to improve the transferability of deep neural networks; describes a quantitative method for estimating the discrepancy between the source and target data to enhance image classification performance; presents a technique for multi-modal fusion that enhances facial action recognition, and a framework for intuition learning in domain adaptation; examines an original interpolation-based approach to address the issue of tracking model degradation in correlation filter-based methods. This authoritative work will serve as an invaluable reference for researchers and practitioners interested in machine learning-based visual recognition and understanding.

Dissertation Abstracts International

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Author :
language : en
Publisher:
Release Date : 2008

Dissertation Abstracts International written by and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2008 with Dissertations, Academic categories.

Adapting Instruction To The Learning Needs Of Children In The Intermediate Grades

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Author : Donald DeWitt Durrell
language : en
Publisher:
Release Date : 1959

Adapting Instruction To The Learning Needs Of Children In The Intermediate Grades written by Donald DeWitt Durrell and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 1959 with Education categories.