Transferrable Representations For Visual Recognition

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Transferrable Representations for Visual Recognition

Author : Jeffrey Donahue
Publisher :
ISBN 13 :
Total Pages : 120 pages
Book Rating : 4.:/5 (13 download)

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Book Synopsis Transferrable Representations for Visual Recognition by : Jeffrey Donahue

Download or read book Transferrable Representations for Visual Recognition written by Jeffrey Donahue and published by . This book was released on 2005 with total page 120 pages. Available in PDF, EPUB and Kindle. Book excerpt: The rapid progress in visual recognition capabilities over the past several years can be attributed largely to improvements in generic and transferrable feature representations, particularly learned representations based on convolutional networks (convnets) trained “end-to-end” to predict visual semantics given raw pixel intensity values. In this thesis, we analyze the structure of these convnet representations and their generality and transferrability to other tasks and settings. We begin in Chapter 2 by examining the hierarchical semantic structure that naturally emerges in convnet representations from large-scale supervised training, even when this structure is unobserved in the training set. Empirically, the resulting representations generalize surprisingly well to classification in related yet distinct settings. Chapters 3 and 4 showcase the flexibility of convnet-based representations for prediction tasks where the inputs or targets have more complex structure. Chapter 3 focuses on representation transfer to the object detection and semantic segmentation tasks in which objects must be localized within an image, as well as labeled. Chapter 4 augments convnets with recurrent structure to handle recognition problems with sequential inputs (e.g., video activity recognition) or outputs (e.g., image captioning). Across each of these domains, end-to-end fine-tuning of the representation for the target task provides a substantial additional performance benefit. Finally, we address the necessity of label supervision for representation learning. In Chapter 5 we propose an unsupervised learning approach based on generative models, demonstrating that some of the transferrable semantic structure learned by supervised convnets can be learned from images alone.

Learning Transferable Representations for Visual Recognition

Author : Yang Zhang
Publisher :
ISBN 13 :
Total Pages : 128 pages
Book Rating : 4.:/5 (117 download)

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Book Synopsis Learning Transferable Representations for Visual Recognition by : Yang Zhang

Download or read book Learning Transferable Representations for Visual Recognition written by Yang Zhang and published by . This book was released on 2020 with total page 128 pages. Available in PDF, EPUB and Kindle. Book excerpt: In the last half-decade, a new renaissance of machine learning originates from the applications of convolutional neural networks to visual recognition tasks. It is believed that a combination of big curated data and novel deep learning techniques can lead to unprecedented results. However, the increasingly large training data is still a drop in the ocean compared with scenarios in the wild. In this literature, we focus on learning transferable representation in the neural networks to ensure the models stay robust, even given different data distributions. We present three exemplar topics in three chapters, respectively: zero-shot learning, domain adaptation, and generalizable adversarial attack. By zero-shot learning, we enable models to predict labels not seen in the training phase. By domain adaptation, we improve a model’s performance on the target domain by mitigating its discrepancy from a labeled source model, without any target annotation. Finally, the generalization adversarial attack focuses on learning an adversarial camouflage that ideally would work in every possible scenario. Despite sharing the same transfer learning philosophy, each of the proposed topics poses a unique challenge requiring a unique solution. In each chapter, we introduce the problem as well as present our solution to the problem. We also discuss some other researchers’ approaches and compare our solution to theirs in the experiments.

Learning Mid-level Representations for Visual Recognition

Author : Angela Eigenstetter
Publisher :
ISBN 13 :
Total Pages : 0 pages
Book Rating : 4.:/5 (931 download)

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Book Synopsis Learning Mid-level Representations for Visual Recognition by : Angela Eigenstetter

Download or read book Learning Mid-level Representations for Visual Recognition written by Angela Eigenstetter and published by . This book was released on 2015 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Representations of Shape in Object Recognition and Long-Term Visual Memory

Author :
Publisher :
ISBN 13 :
Total Pages : 28 pages
Book Rating : 4.:/5 (227 download)

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Book Synopsis Representations of Shape in Object Recognition and Long-Term Visual Memory by :

Download or read book Representations of Shape in Object Recognition and Long-Term Visual Memory written by and published by . This book was released on 1996 with total page 28 pages. Available in PDF, EPUB and Kindle. Book excerpt: Our research has focused on the mechanisms used in visual object recognition. Recent psychophysical results suggest that human perceivers often rely on viewpoint-specific (view-based) representations in conjunction with normalization procedures. Over the past year we have explored the degree to which view-based representations are also appearance based. Specifically we have found that visual recognition across many tasks is sensitive to changes in image properties such as illumination, color, and material (texture). These results indicate that object representations are information rich and that abstract part-based structural-descriptions will not account for much of human recognition performance. Other work has focused on how view-based representations are organized and function across changes in viewpoint. Using 3D stimuli rotated in depth we have investigated the role of task, ranging from basic-level to subordinate-level discriminations, how view-based representations generalize from known members of a class to unfamiliar members of that class, and how perceptual expertise is acquired and influences recognition strategies. We have also been in vestigating the mechanisms used for discriminating between highly similar objects, : e.g., faces.

Computer Vision – ECCV 2022

Author : Shai Avidan
Publisher : Springer Nature
ISBN 13 : 3031198271
Total Pages : 804 pages
Book Rating : 4.0/5 (311 download)

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Book Synopsis Computer Vision – ECCV 2022 by : Shai Avidan

Download or read book Computer Vision – ECCV 2022 written by Shai Avidan and published by Springer Nature. This book was released on 2022-11-01 with total page 804 pages. Available in PDF, EPUB and Kindle. Book excerpt: The 39-volume set, comprising the LNCS books 13661 until 13699, constitutes the refereed proceedings of the 17th European Conference on Computer Vision, ECCV 2022, held in Tel Aviv, Israel, during October 23–27, 2022. The 1645 papers presented in these proceedings were carefully reviewed and selected from a total of 5804 submissions. The papers deal with topics such as computer vision; machine learning; deep neural networks; reinforcement learning; object recognition; image classification; image processing; object detection; semantic segmentation; human pose estimation; 3d reconstruction; stereo vision; computational photography; neural networks; image coding; image reconstruction; object recognition; motion estimation.

Computer Vision – ECCV 2018

Author : Vittorio Ferrari
Publisher : Springer
ISBN 13 : 3030012611
Total Pages : 851 pages
Book Rating : 4.0/5 (3 download)

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Book Synopsis Computer Vision – ECCV 2018 by : Vittorio Ferrari

Download or read book Computer Vision – ECCV 2018 written by Vittorio Ferrari and published by Springer. This book was released on 2018-10-08 with total page 851 pages. Available in PDF, EPUB and Kindle. Book excerpt: The sixteen-volume set comprising the LNCS volumes 11205-11220 constitutes the refereed proceedings of the 15th European Conference on Computer Vision, ECCV 2018, held in Munich, Germany, in September 2018.The 776 revised papers presented were carefully reviewed and selected from 2439 submissions. The papers are organized in topical sections on learning for vision; computational photography; human analysis; human sensing; stereo and reconstruction; optimization; matching and recognition; video attention; and poster sessions.

Domain Adaptation in Computer Vision with Deep Learning

Author : Hemanth Venkateswara
Publisher : Springer Nature
ISBN 13 : 3030455297
Total Pages : 256 pages
Book Rating : 4.0/5 (34 download)

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Book Synopsis Domain Adaptation in Computer Vision with Deep Learning by : Hemanth Venkateswara

Download or read book Domain Adaptation in Computer Vision with Deep Learning written by Hemanth Venkateswara and published by Springer Nature. This book was released on 2020-08-18 with total page 256 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Deep Learning for Marine Science

Author : Haiyong Zheng
Publisher : Frontiers Media SA
ISBN 13 : 2832549055
Total Pages : 555 pages
Book Rating : 4.8/5 (325 download)

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Book Synopsis Deep Learning for Marine Science by : Haiyong Zheng

Download or read book Deep Learning for Marine Science written by Haiyong Zheng and published by Frontiers Media SA. This book was released on 2024-05-15 with total page 555 pages. Available in PDF, EPUB and Kindle. Book excerpt: Deep learning (DL), mainly composed of deep and complex neural networks such as recurrent network and convolutional network, is an emerging research branch in the field of artificial intelligence and machine learning. DL revolution has a far-reaching impact on all scientific disciplines and every corner of our lives. With continuing technological advances, marine science is entering into the big data era with the exponential growth of information. DL is an effective means of harnessing the power of big data. Combined with unprecedented data from cameras, acoustic recorders, satellite remote sensing, and large model outputs, DL enables scientists to solve complex problems in biology, ecosystems, climate, energy, as well as physical and chemical interactions. Although DL has made great strides, it is still only beginning to emerge in many fields of marine science, especially towards representative applications and best practices for the automatic analysis of marine organisms and marine environments. DL in nowadays' marine science mainly leverages cutting-edge techniques of deep neural networks and massive data which collected by in-situ optical or acoustic imaging sensors for underwater applications, such as plankton classification and coral reef detection. This research topic aims to expand the applications of marine science to cover all aspects of detection, classification, segmentation, localization, and density estimation of marine objects, organisms, and phenomena.

Visual Domain Adaptation in the Deep Learning Era

Author : Gabriela Csurka
Publisher : Springer Nature
ISBN 13 : 3031791754
Total Pages : 182 pages
Book Rating : 4.0/5 (317 download)

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Book Synopsis Visual Domain Adaptation in the Deep Learning Era by : Gabriela Csurka

Download or read book Visual Domain Adaptation in the Deep Learning Era written by Gabriela Csurka and published by Springer Nature. This book was released on 2022-06-06 with total page 182 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Computer Vision – ACCV 2020

Author : Hiroshi Ishikawa
Publisher : Springer Nature
ISBN 13 : 3030695387
Total Pages : 730 pages
Book Rating : 4.0/5 (36 download)

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Book Synopsis Computer Vision – ACCV 2020 by : Hiroshi Ishikawa

Download or read book Computer Vision – ACCV 2020 written by Hiroshi Ishikawa and published by Springer Nature. This book was released on 2021-02-24 with total page 730 pages. Available in PDF, EPUB and Kindle. Book excerpt: The six volume set of LNCS 12622-12627 constitutes the proceedings of the 15th Asian Conference on Computer Vision, ACCV 2020, held in Kyoto, Japan, in November/ December 2020.* The total of 254 contributions was carefully reviewed and selected from 768 submissions during two rounds of reviewing and improvement. The papers focus on the following topics: Part I: 3D computer vision; segmentation and grouping Part II: low-level vision, image processing; motion and tracking Part III: recognition and detection; optimization, statistical methods, and learning; robot vision Part IV: deep learning for computer vision, generative models for computer vision Part V: face, pose, action, and gesture; video analysis and event recognition; biomedical image analysis Part VI: applications of computer vision; vision for X; datasets and performance analysis *The conference was held virtually.

200 Tips for Mastering Generative AI

Author : Rick Spair
Publisher : Rick Spair
ISBN 13 :
Total Pages : 888 pages
Book Rating : 4./5 ( download)

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Book Synopsis 200 Tips for Mastering Generative AI by : Rick Spair

Download or read book 200 Tips for Mastering Generative AI written by Rick Spair and published by Rick Spair. This book was released on with total page 888 pages. Available in PDF, EPUB and Kindle. Book excerpt: In the rapidly evolving landscape of artificial intelligence, Generative AI stands out as a transformative force with the potential to revolutionize industries and reshape our understanding of creativity and automation. From its inception, Generative AI has captured the imagination of researchers, developers, and entrepreneurs, offering unprecedented capabilities in generating new data, simulating complex systems, and solving intricate problems that were once considered beyond the reach of machines. This book, "200 Tips for Mastering Generative AI," is a comprehensive guide designed to empower you with the knowledge and practical insights needed to harness the full potential of Generative AI. Whether you are a seasoned AI practitioner, a curious researcher, a forward-thinking entrepreneur, or a passionate enthusiast, this book provides valuable tips and strategies to navigate the vast and intricate world of Generative AI. We invite you to explore, experiment, and innovate with the knowledge you gain from this book. Together, we can unlock the full potential of Generative AI and shape a future where intelligent machines and human creativity coexist and collaborate in unprecedented ways. Welcome to "200 Tips for Mastering Generative AI." Your journey into the fascinating world of Generative AI begins here.

Computer Vision – ECCV 2020

Author : Andrea Vedaldi
Publisher : Springer Nature
ISBN 13 : 3030585832
Total Pages : 829 pages
Book Rating : 4.0/5 (35 download)

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Book Synopsis Computer Vision – ECCV 2020 by : Andrea Vedaldi

Download or read book Computer Vision – ECCV 2020 written by Andrea Vedaldi and published by Springer Nature. This book was released on 2020-11-18 with total page 829 pages. Available in PDF, EPUB and Kindle. Book excerpt: The 30-volume set, comprising the LNCS books 12346 until 12375, constitutes the refereed proceedings of the 16th European Conference on Computer Vision, ECCV 2020, which was planned to be held in Glasgow, UK, during August 23-28, 2020. The conference was held virtually due to the COVID-19 pandemic. The 1360 revised papers presented in these proceedings were carefully reviewed and selected from a total of 5025 submissions. The papers deal with topics such as computer vision; machine learning; deep neural networks; reinforcement learning; object recognition; image classification; image processing; object detection; semantic segmentation; human pose estimation; 3d reconstruction; stereo vision; computational photography; neural networks; image coding; image reconstruction; object recognition; motion estimation.

Medical Image Computing and Computer Assisted Intervention – MICCAI 2020

Author : Anne L. Martel
Publisher : Springer Nature
ISBN 13 : 3030597105
Total Pages : 849 pages
Book Rating : 4.0/5 (35 download)

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Book Synopsis Medical Image Computing and Computer Assisted Intervention – MICCAI 2020 by : Anne L. Martel

Download or read book Medical Image Computing and Computer Assisted Intervention – MICCAI 2020 written by Anne L. Martel and published by Springer Nature. This book was released on 2020-10-02 with total page 849 pages. Available in PDF, EPUB and Kindle. Book excerpt: The seven-volume set LNCS 12261, 12262, 12263, 12264, 12265, 12266, and 12267 constitutes the refereed proceedings of the 23rd International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2020, held in Lima, Peru, in October 2020. The conference was held virtually due to the COVID-19 pandemic. The 542 revised full papers presented were carefully reviewed and selected from 1809 submissions in a double-blind review process. The papers are organized in the following topical sections: Part I: machine learning methodologies Part II: image reconstruction; prediction and diagnosis; cross-domain methods and reconstruction; domain adaptation; machine learning applications; generative adversarial networks Part III: CAI applications; image registration; instrumentation and surgical phase detection; navigation and visualization; ultrasound imaging; video image analysis Part IV: segmentation; shape models and landmark detection Part V: biological, optical, microscopic imaging; cell segmentation and stain normalization; histopathology image analysis; opthalmology Part VI: angiography and vessel analysis; breast imaging; colonoscopy; dermatology; fetal imaging; heart and lung imaging; musculoskeletal imaging Part VI: brain development and atlases; DWI and tractography; functional brain networks; neuroimaging; positron emission tomography

Computer Vision – ACCV 2018

Author : C.V. Jawahar
Publisher : Springer
ISBN 13 : 3030208702
Total Pages : 767 pages
Book Rating : 4.0/5 (32 download)

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Book Synopsis Computer Vision – ACCV 2018 by : C.V. Jawahar

Download or read book Computer Vision – ACCV 2018 written by C.V. Jawahar and published by Springer. This book was released on 2019-05-24 with total page 767 pages. Available in PDF, EPUB and Kindle. Book excerpt: The six volume set LNCS 11361-11366 constitutes the proceedings of the 14th Asian Conference on Computer Vision, ACCV 2018, held in Perth, Australia, in December 2018. The total of 274 contributions was carefully reviewed and selected from 979 submissions during two rounds of reviewing and improvement. The papers focus on motion and tracking, segmentation and grouping, image-based modeling, dep learning, object recognition object recognition, object detection and categorization, vision and language, video analysis and event recognition, face and gesture analysis, statistical methods and learning, performance evaluation, medical image analysis, document analysis, optimization methods, RGBD and depth camera processing, robotic vision, applications of computer vision.

Development and Analysis of Deep Learning Architectures

Author : Witold Pedrycz
Publisher : Springer Nature
ISBN 13 : 3030317641
Total Pages : 292 pages
Book Rating : 4.0/5 (33 download)

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Book Synopsis Development and Analysis of Deep Learning Architectures by : Witold Pedrycz

Download or read book Development and Analysis of Deep Learning Architectures written by Witold Pedrycz and published by Springer Nature. This book was released on 2019-11-01 with total page 292 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book offers a timely reflection on the remarkable range of algorithms and applications that have made the area of deep learning so attractive and heavily researched today. Introducing the diversity of learning mechanisms in the environment of big data, and presenting authoritative studies in fields such as sensor design, health care, autonomous driving, industrial control and wireless communication, it enables readers to gain a practical understanding of design. The book also discusses systematic design procedures, optimization techniques, and validation processes.

Artificial Neural Networks and Machine Learning – ICANN 2018

Author : Věra Kůrková
Publisher : Springer
ISBN 13 : 303001424X
Total Pages : 866 pages
Book Rating : 4.0/5 (3 download)

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Book Synopsis Artificial Neural Networks and Machine Learning – ICANN 2018 by : Věra Kůrková

Download or read book Artificial Neural Networks and Machine Learning – ICANN 2018 written by Věra Kůrková and published by Springer. This book was released on 2018-10-02 with total page 866 pages. Available in PDF, EPUB and Kindle. Book excerpt: This three-volume set LNCS 11139-11141 constitutes the refereed proceedings of the 27th International Conference on Artificial Neural Networks, ICANN 2018, held in Rhodes, Greece, in October 2018. The papers presented in these volumes was carefully reviewed and selected from total of 360 submissions. They are related to the following thematic topics: AI and Bioinformatics, Bayesian and Echo State Networks, Brain Inspired Computing, Chaotic Complex Models, Clustering, Mining, Exploratory Analysis, Coding Architectures, Complex Firing Patterns, Convolutional Neural Networks, Deep Learning (DL), DL in Real Time Systems, DL and Big Data Analytics, DL and Big Data, DL and Forensics, DL and Cybersecurity, DL and Social Networks, Evolving Systems – Optimization, Extreme Learning Machines, From Neurons to Neuromorphism, From Sensation to Perception, From Single Neurons to Networks, Fuzzy Modeling, Hierarchical ANN, Inference and Recognition, Information and Optimization, Interacting with The Brain, Machine Learning (ML), ML for Bio Medical systems, ML and Video-Image Processing, ML and Forensics, ML and Cybersecurity, ML and Social Media, ML in Engineering, Movement and Motion Detection, Multilayer Perceptrons and Kernel Networks, Natural Language, Object and Face Recognition, Recurrent Neural Networks and Reservoir Computing, Reinforcement Learning, Reservoir Computing, Self-Organizing Maps, Spiking Dynamics/Spiking ANN, Support Vector Machines, Swarm Intelligence and Decision-Making, Text Mining, Theoretical Neural Computation, Time Series and Forecasting, Training and Learning.

Feature and Dimensionality Reduction for Clustering with Deep Learning

Author : Frederic Ros
Publisher : Springer Nature
ISBN 13 : 3031487435
Total Pages : 273 pages
Book Rating : 4.0/5 (314 download)

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Book Synopsis Feature and Dimensionality Reduction for Clustering with Deep Learning by : Frederic Ros

Download or read book Feature and Dimensionality Reduction for Clustering with Deep Learning written by Frederic Ros and published by Springer Nature. This book was released on 2024-01-22 with total page 273 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book presents an overview of recent methods of feature selection and dimensionality reduction that are based on Deep Neural Networks (DNNs) for a clustering perspective, with particular attention to the knowledge discovery question. The authors first present a synthesis of the major recent influencing techniques and "tricks" participating in recent advances in deep clustering, as well as a recall of the main deep learning architectures. Secondly, the book highlights the most popular works by “family” to provide a more suitable starting point from which to develop a full understanding of the domain. Overall, the book proposes a comprehensive up-to-date review of deep feature selection and deep clustering methods with particular attention to the knowledge discovery question and under a multi-criteria analysis. The book can be very helpful for young researchers, non-experts, and R&D AI engineers.