Domain Adaptation In Computer Vision With Deep Learning

Author : Hemanth Venkateswara,Sethuraman Panchanathan
Publisher : Springer Nature
Page : 256 pages
File Size : 53,7 Mb
Release : 2020-08-18
Category : Computers
ISBN : 9783030455293

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Domain Adaptation in Computer Vision with Deep Learning by Hemanth Venkateswara,Sethuraman Panchanathan Pdf

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.

Author : Gabriela Csurka,Timothy M. Hospedales,Mathieu Salzmann,Tatiana Tommasi
Publisher : Springer Nature
Page : 182 pages
File Size : 51,8 Mb
Release : 2022-06-06
Category : Computers
ISBN : 9783031791758

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Visual Domain Adaptation in the Deep Learning Era by Gabriela Csurka,Timothy M. Hospedales,Mathieu Salzmann,Tatiana Tommasi Pdf

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.

Author : Gabriela Csurka
Publisher : Springer
Page : 344 pages
File Size : 55,9 Mb
Release : 2017-09-10
Category : Computers
ISBN : 9783319583471

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Domain Adaptation in Computer Vision Applications by Gabriela Csurka Pdf

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.

Author : Richa Singh,Mayank Vatsa,Vishal M. Patel,Nalini Ratha
Publisher : Springer Nature
Page : 144 pages
File Size : 40,7 Mb
Release : 2020-01-08
Category : Computers
ISBN : 9783030306717

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Domain Adaptation for Visual Understanding by Richa Singh,Mayank Vatsa,Vishal M. Patel,Nalini Ratha Pdf

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.

Author : Anonim
Publisher : BoD – Books on Demand
Page : 124 pages
File Size : 54,6 Mb
Release : 2020-12-09
Category : Computers
ISBN : 9781839628788

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Advances and Applications in Deep Learning by Anonim Pdf

Artificial Intelligence (AI) has attracted the attention of researchers and users alike and is taking an increasingly crucial role in our modern society. From cars, smartphones, and airplanes to medical equipment, consumer applications, and industrial machines, the impact of AI is notoriously changing the world we live in. In this context, Deep Learning (DL) is one of the techniques that has taken the lead for cognitive processes, pattern recognition, object detection, and machine learning, all of which have played a crucial role in the growth of AI. As such, this book examines DL applications and future trends in the field. It is a useful resource for researchers and students alike.

Author : Jingjing Li
Publisher : Springer Nature
Page : 234 pages
File Size : 44,9 Mb
Release : 2024-06-23
Category : Electronic
ISBN : 9789819710256

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Unsupervised Domain Adaptation by Jingjing Li Pdf

Author : Qian Wang,Fausto Milletari,Hien V. Nguyen,Shadi Albarqouni,M. Jorge Cardoso,Nicola Rieke,Ziyue Xu,Konstantinos Kamnitsas,Vishal Patel,Badri Roysam,Steve Jiang,Kevin Zhou,Khoa Luu,Ngan Le
Publisher : Springer Nature
Page : 254 pages
File Size : 53,5 Mb
Release : 2019-10-13
Category : Computers
ISBN : 9783030333911

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Domain Adaptation and Representation Transfer and Medical Image Learning with Less Labels and Imperfect Data by Qian Wang,Fausto Milletari,Hien V. Nguyen,Shadi Albarqouni,M. Jorge Cardoso,Nicola Rieke,Ziyue Xu,Konstantinos Kamnitsas,Vishal Patel,Badri Roysam,Steve Jiang,Kevin Zhou,Khoa Luu,Ngan Le Pdf

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.

Author : Shadi Albarqouni,Spyridon Bakas,Konstantinos Kamnitsas,M. Jorge Cardoso,Bennett Landman,Wenqi Li,Fausto Milletari,Nicola Rieke,Holger Roth,Daguang Xu,Ziyue Xu
Publisher : Springer Nature
Page : 224 pages
File Size : 49,5 Mb
Release : 2020-09-25
Category : Computers
ISBN : 9783030605483

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Domain Adaptation and Representation Transfer, and Distributed and Collaborative Learning by Shadi Albarqouni,Spyridon Bakas,Konstantinos Kamnitsas,M. Jorge Cardoso,Bennett Landman,Wenqi Li,Fausto Milletari,Nicola Rieke,Holger Roth,Daguang Xu,Ziyue Xu Pdf

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. The conference was planned to take place in Lima, Peru, but changed to an online format due to the Coronavirus pandemic. For DART 2020, 12 full papers were accepted from 18 submissions. They deal with methodological advancements and ideas that can improve the applicability of machine learning (ML)/deep learning (DL) approaches to clinical settings by making them robust and consistent across different domains. For DCL 2020, the 8 papers included in this book were accepted from a total of 12 submissions. They focus on the comparison, evaluation and discussion of methodological advancement and practical ideas about machine learning applied to problems where data cannot be stored in centralized databases; where information privacy is a priority; where it is necessary to deliver strong guarantees on the amount and nature of private information that may be revealed by the model as a result of training; and where it's necessary to orchestrate, manage and direct clusters of nodes participating in the same learning task.

Author : Lisa Koch,M. Jorge Cardoso,Enzo Ferrante,Konstantinos Kamnitsas,Mobarakol Islam,Meirui Jiang,Nicola Rieke,Sotirios A. Tsaftaris,Dong Yang
Publisher : Springer Nature
Page : 180 pages
File Size : 41,6 Mb
Release : 2023-10-13
Category : Computers
ISBN : 9783031458576

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Domain Adaptation and Representation Transfer by Lisa Koch,M. Jorge Cardoso,Enzo Ferrante,Konstantinos Kamnitsas,Mobarakol Islam,Meirui Jiang,Nicola Rieke,Sotirios A. Tsaftaris,Dong Yang Pdf

This book constitutes the refereed proceedings of the 5th MICCAI Workshop on Domain Adaptation and Representation Transfer, DART 2023, which was held in conjunction with MICCAI 2023, in October 2023. The 16 full papers presented in this book were carefully reviewed and selected from 32 submissions. They 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.

Author : Shadi Albarqouni,M. Jorge Cardoso,Qi Dou,Konstantinos Kamnitsas,Bishesh Khanal,Islem Rekik,Nicola Rieke,Debdoot Sheet,Sotirios Tsaftaris,Daguang Xu,Ziyue Xu
Publisher : Springer Nature
Page : 276 pages
File Size : 45,9 Mb
Release : 2021-09-23
Category : Computers
ISBN : 9783030877224

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Domain Adaptation and Representation Transfer, and Affordable Healthcare and AI for Resource Diverse Global Health by Shadi Albarqouni,M. Jorge Cardoso,Qi Dou,Konstantinos Kamnitsas,Bishesh Khanal,Islem Rekik,Nicola Rieke,Debdoot Sheet,Sotirios Tsaftaris,Daguang Xu,Ziyue Xu Pdf

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.

Author : Massimo Tistarelli,Shiv Ram Dubey,Satish Kumar Singh,Xiaoyi Jiang
Publisher : Springer Nature
Page : 777 pages
File Size : 49,7 Mb
Release : 2023-05-05
Category : Technology & Engineering
ISBN : 9789811978678

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Computer Vision and Machine Intelligence by Massimo Tistarelli,Shiv Ram Dubey,Satish Kumar Singh,Xiaoyi Jiang Pdf

This book presents selected research papers on current developments in the fields of computer vision and machine intelligence from International Conference on Computer Vision and Machine Intelligence (CVMI 2022). The book covers topics in image processing, artificial intelligence, machine learning, deep learning, computer vision, machine intelligence, etc. The book is useful for researchers, postgraduate and undergraduate students, and professionals working in this domain.

Author : Benjamin Planche,Eliot Andres
Publisher : Packt Publishing Ltd
Page : 363 pages
File Size : 50,8 Mb
Release : 2019-05-30
Category : Computers
ISBN : 9781788839266

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Hands-On Computer Vision with TensorFlow 2 by Benjamin Planche,Eliot Andres Pdf

A practical guide to building high performance systems for object detection, segmentation, video processing, smartphone applications, and more Key FeaturesDiscover how to build, train, and serve your own deep neural networks with TensorFlow 2 and KerasApply modern solutions to a wide range of applications such as object detection and video analysisLearn how to run your models on mobile devices and web pages and improve their performanceBook Description Computer vision solutions are becoming increasingly common, making their way into fields such as health, automobile, social media, and robotics. This book will help you explore TensorFlow 2, the brand new version of Google's open source framework for machine learning. You will understand how to benefit from using convolutional neural networks (CNNs) for visual tasks. Hands-On Computer Vision with TensorFlow 2 starts with the fundamentals of computer vision and deep learning, teaching you how to build a neural network from scratch. You will discover the features that have made TensorFlow the most widely used AI library, along with its intuitive Keras interface. You'll then move on to building, training, and deploying CNNs efficiently. Complete with concrete code examples, the book demonstrates how to classify images with modern solutions, such as Inception and ResNet, and extract specific content using You Only Look Once (YOLO), Mask R-CNN, and U-Net. You will also build generative adversarial networks (GANs) and variational autoencoders (VAEs) to create and edit images, and long short-term memory networks (LSTMs) to analyze videos. In the process, you will acquire advanced insights into transfer learning, data augmentation, domain adaptation, and mobile and web deployment, among other key concepts. By the end of the book, you will have both the theoretical understanding and practical skills to solve advanced computer vision problems with TensorFlow 2.0. What you will learnCreate your own neural networks from scratchClassify images with modern architectures including Inception and ResNetDetect and segment objects in images with YOLO, Mask R-CNN, and U-NetTackle problems faced when developing self-driving cars and facial emotion recognition systemsBoost your application's performance with transfer learning, GANs, and domain adaptationUse recurrent neural networks (RNNs) for video analysisOptimize and deploy your networks on mobile devices and in the browserWho this book is for If you're new to deep learning and have some background in Python programming and image processing, like reading/writing image files and editing pixels, this book is for you. Even if you're an expert curious about the new TensorFlow 2 features, you'll find this book useful. While some theoretical concepts require knowledge of algebra and calculus, the book covers concrete examples focused on practical applications such as visual recognition for self-driving cars and smartphone apps.

Author : E. R. Davies,Matthew Turk
Publisher : Elsevier
Page : 582 pages
File Size : 54,5 Mb
Release : 2021-11-12
Category : Computers
ISBN : 9780128221099

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Advanced Methods and Deep Learning in Computer Vision by E. R. Davies,Matthew Turk Pdf

Advanced Methods and Deep Learning in Computer Vision presents advanced computer vision methods, emphasizing machine learning and deep learning techniques that have emerged during the past 5-10 years. The book provides clear explanations of principles and algorithms supported with applications. Topics covered include machine learning, deep learning networks, generative adversarial networks, deep reinforcement learning, self-supervised learning, extraction of robust features, object detection, semantic segmentation, linguistic descriptions of images, visual search, visual tracking, 3D shape retrieval, and anomaly detection. This book provides easy learning for researchers and practitioners of advanced computer vision methods, but it is also suitable as a textbook for a second course on computer vision and deep learning for advanced undergraduates and graduate students. Key Features . Provides an important reference on deep learning and advanced computer vision methods that were created by leaders in the field . Illustrates principles with modern, real-world applications . Suitable for self-learning or as a text for graduate courses About the Editors Roy Davies is Emeritus Professor of Machine Vision at Royal Holloway, University of London. He has worked on many aspects of vision, from feature detection to robust, real-time implementations of practical vision tasks. His interests include automated visual inspection, surveillance, vehicle guidance, crime detection, and neural networks. He has published more than 200 papers, and three books. The first, published in 1990, has been widely used internationally for more than 25 years: in 2017 it came out in a fifth edition entitled Computer Vision, Principles, Algorithms, Applications, Learning. Roy holds a DSc at the University of London and has been awarded Distinguished Fellow of the British Machine Vision Association, and Fellow of the International Association of Pattern Recognition. Matthew Turk is the President of the Toyota Technological Institute at Chicago (TTIC) and an Emeritus Professor at the University of California, Santa Barbara. He has received several best paper awards and was named a Fellow of the Institute of Electrical and Electronics Engineers (IEEE) in 2013, the International Association for Pattern Recognition (IAPR) in 2014, and the Association for Computing Machinery (ACM) in 2020, for contributions to computer vision, face recognition, and multimodal interaction.

Author : Sergey I. Nikolenko
Publisher : Springer Nature
Page : 348 pages
File Size : 48,8 Mb
Release : 2021-06-26
Category : Computers
ISBN : 9783030751784

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Synthetic Data for Deep Learning by Sergey I. Nikolenko Pdf

This is the first book on synthetic data for deep learning, and its breadth of coverage may render this book as the default reference on synthetic data for years to come. The book can also serve as an introduction to several other important subfields of machine learning that are seldom touched upon in other books. Machine learning as a discipline would not be possible without the inner workings of optimization at hand. The book includes the necessary sinews of optimization though the crux of the discussion centers on the increasingly popular tool for training deep learning models, namely synthetic data. It is expected that the field of synthetic data will undergo exponential growth in the near future. This book serves as a comprehensive survey of the field. In the simplest case, synthetic data refers to computer-generated graphics used to train computer vision models. There are many more facets of synthetic data to consider. In the section on basic computer vision, the book discusses fundamental computer vision problems, both low-level (e.g., optical flow estimation) and high-level (e.g., object detection and semantic segmentation), synthetic environments and datasets for outdoor and urban scenes (autonomous driving), indoor scenes (indoor navigation), aerial navigation, and simulation environments for robotics. Additionally, it touches upon applications of synthetic data outside computer vision (in neural programming, bioinformatics, NLP, and more). It also surveys the work on improving synthetic data development and alternative ways to produce it such as GANs. The book introduces and reviews several different approaches to synthetic data in various domains of machine learning, most notably the following fields: domain adaptation for making synthetic data more realistic and/or adapting the models to be trained on synthetic data and differential privacy for generating synthetic data with privacy guarantees. This discussion is accompanied by an introduction into generative adversarial networks (GAN) and an introduction to differential privacy.

Author : Xudong Mao,Qing Li
Publisher : Springer Nature
Page : 77 pages
File Size : 47,7 Mb
Release : 2021-03-21
Category : Computers
ISBN : 9789813360488

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Generative Adversarial Networks for Image Generation by Xudong Mao,Qing Li Pdf

Generative adversarial networks (GANs) were introduced by Ian Goodfellow and his co-authors including Yoshua Bengio in 2014, and were to referred by Yann Lecun (Facebook’s AI research director) as “the most interesting idea in the last 10 years in ML.” GANs’ potential is huge, because they can learn to mimic any distribution of data, which means they can be taught to create worlds similar to our own in any domain: images, music, speech, prose. They are robot artists in a sense, and their output is remarkable – poignant even. In 2018, Christie’s sold a portrait that had been generated by a GAN for $432,000. Although image generation has been challenging, GAN image generation has proved to be very successful and impressive. However, there are two remaining challenges for GAN image generation: the quality of the generated image and the training stability. This book first provides an overview of GANs, and then discusses the task of image generation and the details of GAN image generation. It also investigates a number of approaches to address the two remaining challenges for GAN image generation. Additionally, it explores three promising applications of GANs, including image-to-image translation, unsupervised domain adaptation and GANs for security. This book appeals to students and researchers who are interested in GANs, image generation and general machine learning and computer vision.