Composing Fisher Kernels From Deep Neural Models

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Composing Fisher Kernels From Deep Neural Models

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Author : Tayyaba Azim
language : en
Publisher: Springer
Release Date : 2018-08-23

Composing Fisher Kernels From Deep Neural Models written by Tayyaba Azim and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018-08-23 with Computers categories.

This book shows machine learning enthusiasts and practitioners how to get the best of both worlds by deriving Fisher kernels from deep learning models. In addition, the book shares insight on how to store and retrieve large-dimensional Fisher vectors using feature selection and compression techniques. Feature selection and feature compression are two of the most popular off-the-shelf methods for reducing data’s high-dimensional memory footprint and thus making it suitable for large-scale visual retrieval and classification. Kernel methods long remained the de facto standard for solving large-scale object classification tasks using low-level features, until the revival of deep models in 2006. Later, they made a comeback with improved Fisher vectors in 2010. However, their supremacy was always challenged by various versions of deep models, now considered to be the state of the art for solving various machine learning and computer vision tasks. Although the two research paradigms differ significantly, the excellent performance of Fisher kernels on the Image Net large-scale object classification dataset has caught the attention of numerous kernel practitioners, and many have drawn parallels between the two frameworks for improving the empirical performance on benchmark classification tasks. Exploring concrete examples on different data sets, the book compares the computational and statistical aspects of different dimensionality reduction approaches and identifies metrics to show which approach is superior to the other for Fisher vector encodings. It also provides references to some of the most useful resources that could provide practitioners and machine learning enthusiasts a quick start for learning and implementing a variety of deep learning models and kernel functions.

Domain Adaptation And Representation Transfer

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Author : Lisa Koch
language : en
Publisher: Springer Nature
Release Date : 2023-10-13

Domain Adaptation And Representation Transfer written by Lisa Koch and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-10-13 with Computers categories.

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.

Graph Based Keyword Spotting

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Author : Michael Stauffer
language : en
Publisher: World Scientific
Release Date : 2019-07-24

Graph Based Keyword Spotting written by Michael Stauffer and has been published by World Scientific this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-07-24 with Computers categories.

Keyword Spotting (KWS) has been proposed as a flexible and more error-tolerant alternative to full transcriptions. In most cases, it allows to retrieve arbitrary query words in handwritten historical document.This comprehensive compendium gives a self-contained preamble and visually attractive description to the field of graph-based KWS. The volume highlights a profound insight into each step of the whole KWS pipeline, viz. image preprocessing, graph representation and graph matching.Written by two world-renowned co-authors, this unique title combines two very current research fields of graph-based pattern recognition and document analysis. The book serves as an attractive teaching material for graduate students, as well as a useful reference text for professionals, academics and researchers.

Deep Generative Models

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Author : Anirban Mukhopadhyay
language : en
Publisher: Springer Nature
Release Date : 2024-10-08

Deep Generative Models written by Anirban Mukhopadhyay and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-10-08 with Computers categories.

This book constitutes the proceedings of the 4th workshop on Deep Generative Models for Medical Image Computing and Computer Assisted Intervention, DGM4MICCAI 2024, held in conjunction with the 27th International conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2024, in Marrakesh, Morocco in October 2024. The 21 papers presented here were carefully reviewed and selected from 40 submissions. These papers deal with a broad range of topics, ranging from methodology (such as Causal inference, Latent interpretation, Generative factor analysis) to Applications (such as Mammography, Vessel imaging, Surgical videos and more).

Differences In Shale Oil And Gas Reservoirs Across Various Sedimentary Environments Theories And Applications Volume Ii

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Author : Hu Li
language : en
Publisher: Frontiers Media SA
Release Date : 2025-06-27

Differences In Shale Oil And Gas Reservoirs Across Various Sedimentary Environments Theories And Applications Volume Ii written by Hu Li 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 2025-06-27 with Science categories.

This Research Topic is Volume II of a series. The previous volume can be found here: Differences in Shale Oil and Gas Reservoirs across Various Sedimentary Environments: Theories and Applications The remarkable success of shale oil and gas production in North America has sparked worldwide interest in its significance. Notably, substantial shale oil and gas reserves have been discovered in China’s Cambrian and Ordovician-Silurian shales, which serve as the primary sources of production. Across the Asian continent, other shale plays exist, with several countries such as India, Saudi Arabia, and Pakistan actively pursuing development plans to identify additional resources. Globally, exploration and development of shale oil and gas in marine-continental transitional and terrestrial formations have resulted in significant breakthroughs, leading to the development of a host of geological theories and technologies for shale oil and gas extraction. With the availability of sophisticated exploration, drilling, logging, and advanced analysis and testing tools, in-depth investigation can be conducted on various aspects of shale formations, including the organic matter enrichment mechanism, sedimentation sequence, reservoir formation, oil and gas generation, drilling, and development. Additionally, the coexistence of similarities and differences in the characteristics of shale reservoirs formed in different sedimentary environments will undoubtedly impact the exploration and development of shale oil and gas. This Research Topic aims to bring together Original Research and Review articles addressing the similarities and differences of the geological theories of shale oil and gas in terrestrial, marine, and marine-continental transitional formations, which facilitates an overview of the latest advancement in how these geological theories can be applied in major shale oil and gas basins worldwide. Potential themes include, but are not limited to: • Management of global shale oil and gas development • Fractures and faults in shale • Mechanisms of organic natter enrichment • Stratification and sedimentary characteristics of shale deposits • Quantitative characterization of shale reservoirs • Pore space characterization of shale reservoirs • Shale oil and gas preservation conditions • Reservoir formation mechanisms of shale oil and gas • Drilling and development of shale oil and gas in different facies • Tight gas reservoir formation and conversion • Variations in shale reservoir characteristics

Computer Vision Eccv 2016

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Author : Bastian Leibe
language : en
Publisher: Springer
Release Date : 2016-09-16

Computer Vision Eccv 2016 written by Bastian Leibe and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2016-09-16 with Computers categories.

The eight-volume set comprising LNCS volumes 9905-9912 constitutes the refereed proceedings of the 14th European Conference on Computer Vision, ECCV 2016, held in Amsterdam, The Netherlands, in October 2016. The 415 revised papers presented were carefully reviewed and selected from 1480 submissions. The papers cover all aspects of computer vision and pattern recognition such as 3D computer vision; computational photography, sensing and display; face and gesture; low-level vision and image processing; motion and tracking; optimization methods; physics-based vision, photometry and shape-from-X; recognition: detection, categorization, indexing, matching; segmentation, grouping and shape representation; statistical methods and learning; video: events, activities and surveillance; applications. They are organized in topical sections on detection, recognition and retrieval; scene understanding; optimization; image and video processing; learning; action, activity and tracking; 3D; and 9 poster sessions.

Deep Learning For Multimedia Processing Applications

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Author : Uzair Aslam Bhatti
language : en
Publisher: CRC Press
Release Date : 2024-02-21

Deep Learning For Multimedia Processing Applications written by Uzair Aslam Bhatti and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-02-21 with Computers categories.

Deep Learning for Multimedia Processing Applications is a comprehensive guide that explores the revolutionary impact of deep learning techniques in the field of multimedia processing. Written for a wide range of readers, from students to professionals, this book offers a concise and accessible overview of the application of deep learning in various multimedia domains, including image processing, video analysis, audio recognition, and natural language processing. Divided into two volumes, Volume Two delves into advanced topics such as convolutional neural networks (CNNs), recurrent neural networks (RNNs), and generative adversarial networks (GANs), explaining their unique capabilities in multimedia tasks. Readers will discover how deep learning techniques enable accurate and efficient image recognition, object detection, semantic segmentation, and image synthesis. The book also covers video analysis techniques, including action recognition, video captioning, and video generation, highlighting the role of deep learning in extracting meaningful information from videos. Furthermore, the book explores audio processing tasks such as speech recognition, music classification, and sound event detection using deep learning models. It demonstrates how deep learning algorithms can effectively process audio data, opening up new possibilities in multimedia applications. Lastly, the book explores the integration of deep learning with natural language processing techniques, enabling systems to understand, generate, and interpret textual information in multimedia contexts. Throughout the book, practical examples, code snippets, and real-world case studies are provided to help readers gain hands-on experience in implementing deep learning solutions for multimedia processing. Deep Learning for Multimedia Processing Applications is an essential resource for anyone interested in harnessing the power of deep learning to unlock the vast potential of multimedia data.

Intelligent Systems And Applications

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Author : Kohei Arai
language : en
Publisher: Springer Nature
Release Date : 2021-08-02

Intelligent Systems And Applications written by Kohei Arai and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-08-02 with Technology & Engineering categories.

This book presents Proceedings of the 2021 Intelligent Systems Conference which is a remarkable collection of chapters covering a wider range of topics in areas of intelligent systems and artificial intelligence and their applications to the real world. The conference attracted a total of 496 submissions from many academic pioneering researchers, scientists, industrial engineers, and students from all around the world. These submissions underwent a double-blind peer-review process. Of the total submissions, 180 submissions have been selected to be included in these proceedings. As we witness exponential growth of computational intelligence in several directions and use of intelligent systems in everyday applications, this book is an ideal resource for reporting latest innovations and future of AI. The chapters include theory and application on all aspects of artificial intelligence, from classical to intelligent scope. We hope that readers find the book interesting and valuable; it provides the state-of-the-art intelligent methods and techniques for solving real-world problems along with a vision of the future research.

Applications Of Artificial Intelligence Machine Learning And Deep Learning In Plant Breeding

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Author : Maliheh Eftekhari
language : en
Publisher: Frontiers Media SA
Release Date : 2024-05-29

Applications Of Artificial Intelligence Machine Learning And Deep Learning In Plant Breeding written by Maliheh Eftekhari 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 2024-05-29 with Science categories.

Artificial Intelligence (AI) is an extensive concept that can be interpreted as a concentration on designing computer programs to train machines to accomplish functions like or better than hu-mans. An important subset of AI is Machine Learning (ML), in which a computer is provided with the capacity to learn its own patterns instead of the patterns and restrictions set by a human programmer, thus improving from experience. Deep Learning (DL), as a class of ML techniques, employs multilayered neural networks. The application of AI to plant science research is new and has grown significantly in recent years due to developments in calculation power, proficien-cies of hardware, and software progress. AI algorithms try to provide classifications and predic-tions. As applied to plant breeding, particularly omics data, ML as a given AI algorithm tries to translate omics data, which are intricate and include nonlinear interactions, into precise plant breeding. The applications of AI are extending rapidly and enhancing intensely in sophistication owing to the capability of rapid processing of huge and heterogeneous data. The conversion of AI techniques into accurate plant breeding is of great importance and will play a key role in the new era of plant breeding techniques in the coming years, particularly multi-omics data analysis. Advancements in plant breeding mainly depend upon developing statistical methods that harness the complicated data provided by analytical technologies identifying and quantifying genes, transcripts, proteins, metabolites, etc. The systems biology approach used in plant breeding, which integrates genomics, transcriptomics, proteomics, metabolomics, and other omics data, provides a massive amount of information. It is essential to perform accurate statistical analyses and AI methods such as ML and DL as well as optimization techniques to not only achieve an understanding of networks regulation and plant cell functions but develop high-precision models to predict the reaction of new Genetically Modified (GM) plants in special conditions. The constructed models will be of great economic importance, significantly reducing the time, labor, and instrument costs when finding optimized conditions for the bio-exploitation of plants. This Research Topic covers a wide range of studies on artificial intelligence-assisted plant breeding techniques, which contribute to plant biology and plant omics research. The relevant sub-topics include, but are not restricted to, the following: • AI-assisted plant breeding using omics and multi-omics approaches • Applying AI techniques along with multi-omics to recognize novel biomarkers associated with plant biological activities • Constructing up-to-date ML modeling and analyzing methods for dealing with omics data related to different plant growth processes • AI-assisted omics techniques in the plant defense process • Combining AI-assisted omics and multi-omics techniques using plant system biology approaches • Combining bioinformatics tools with AI approaches to analyze plant omics data • Designing cutting-edge workflow and developing innovative AI biology methods for omics data analysis

Key Technologies For On Demand 6g Network Services

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Author : Jianxin Liao
language : en
Publisher: Springer Nature
Release Date : 2024-09-25

Key Technologies For On Demand 6g Network Services written by Jianxin Liao and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-09-25 with Computers categories.

This book delves into the confluence of AI and the transformative potential it holds for the future of 6G network services. It uncovers how the integration of AI technologies as well as redefines the landscape of network management and control. This book also offers a new paradigm for delivering on-demand services that are immersive, personalized and of ultimate performance. A detailed exploration of AI-driven network management systems is presenting in this book, discussing the development of knowledge-defined networking, the construction of all-scenario on-demand service systems and the critical role of network management and control in achieving 6G’s vision. This book begins by examining the historical evolution of communication networks and the pivotal shift towards technology-driven demands in the 6G era. It outlines the book’s coverage of the foundational theories, wireless technologies as well as network architectures that will underpin the next generation of mobile services. Further, this book provides a comprehensive analysis of the key technologies required to support 6G on-demand services, such as trusted and autonomous access control, intelligent resource allocation and service capability coordination. It discusses the challenges and opportunities in developing a network that is not only high-performing but also adaptable to a wide range of applications, from personal use to industrial and agricultural production, and public services. This book targets advanced-level students and researchers working in this field serving as both a technical guide and a visionary outlook on the role of AI in shaping 6G networks. It also offers insights into the research, development, and potential applications of AI in network services, making it an invaluable resource for professionals, who understand or contribute to the advancement of 6G technologies.