[PDF] Composing Fisher Kernels From Deep Neural Models eBook

Author : Tayyaba Azim
Publisher : Springer
Page : 69 pages
File Size : 41,13 MB
Release : 2018-08-23
Category : Computers
ISBN : 3319985248

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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.

Author : Lisa Koch
Publisher : Springer Nature
Page : 180 pages
File Size : 17,77 MB
Release : 2023-10-13
Category : Computers
ISBN : 3031458575

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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 : Bastian Leibe
Publisher : Springer
Page : 910 pages
File Size : 22,40 MB
Release : 2016-09-16
Category : Computers
ISBN : 3319464663

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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.

Author : Youngmin Cho
Publisher :
Page : 103 pages
File Size : 21,72 MB
Release : 2012
Category :
ISBN : 9781267433251

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We introduce a new family of positive-definite kernels that mimic the computation in large neural networks. We derive the different members of this family by considering neural networks with different activation functions. Using these kernels as building blocks, we also show how to construct other positive-definite kernels by operations such as composition, multiplication, and averaging. We explore the use of these kernels in standard models of supervised learning, such as support vector machines for large margin classification, as well as in new models of unsupervised learning based on deep architectures. On several problems, we obtain better results than previous, leading benchmarks from both support vector machines with Gaussian kernels as well as deep belief nets. Finally, we examine the properties of these kernels by analyzing the geometry of surfaces that they induce in Hilbert space.

Author : Liangqu Long
Publisher :
Page : 0 pages
File Size : 41,92 MB
Release : 2022
Category :
ISBN : 9781484284049

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Incorporate deep learning into your development projects through hands-on coding and the latest versions of deep learning software, such as TensorFlow 2 and Keras. The materials used in this book are based on years of successful online education experience and feedback from thousands of online learners. You'll start with an introduction to AI, where you'll learn the history of neural networks and what sets deep learning apart from other varieties of machine learning. Discovery the variety of deep learning frameworks and set-up a deep learning development environment. Next, you'll jump into simple classification programs for hand-writing analysis. Once you've tackled the basics of deep learning, you move on to TensorFlow 2 specifically. Find out what exactly a Tensor is and how to work with MNIST datasets. Finally, you'll get into the heavy lifting of programming neural networks and working with a wide variety of neural network types such as GANs and RNNs. Deep Learning is a new area of Machine Learning research widely used in popular applications, such as voice assistant and self-driving cars. Work through the hands-on material in this book and become a TensorFlow programmer! You will: Develop using deep learning algorithms Build deep learning models using TensorFlow 2 Create classification systems and other, practical deep learning applications.

Author : Uzair Aslam Bhatti
Publisher : CRC Press
Page : 481 pages
File Size : 38,42 MB
Release : 2024-02-21
Category : Computers
ISBN : 1003828051

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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.

Author : Stauffer Michael
Publisher : World Scientific
Page : 296 pages
File Size : 14,96 MB
Release : 2019-07-24
Category : Computers
ISBN : 9811206643

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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.

Author : Varun Bajaj
Publisher : Academic Press
Page : 394 pages
File Size : 17,66 MB
Release : 2022-02-04
Category : Science
ISBN : 0323914128

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Artificial Intelligence-Based Brain Computer Interface provides concepts of AI for the modeling of non-invasive modalities of medical signals such as EEG, MRI and FMRI. These modalities and their AI-based analysis are employed in BCI and related applications. The book emphasizes the real challenges in non-invasive input due to the complex nature of the human brain and for a variety of applications for analysis, classification and identification of different mental states. Each chapter starts with a description of a non-invasive input example and the need and motivation of the associated AI methods, along with discussions to connect the technology through BCI. Major topics include different AI methods/techniques such as Deep Neural Networks and Machine Learning algorithms for different non-invasive modalities such as EEG, MRI, FMRI for improving the diagnosis and prognosis of numerous disorders of the nervous system, cardiovascular system, musculoskeletal system, respiratory system and various organs of the body. The book also covers applications of AI in the management of chronic conditions, databases, and in the delivery of health services. Provides readers with an understanding of key applications of Artificial Intelligence to Brain-Computer Interface for acquisition and modelling of non-invasive biomedical signal and image modalities for various conditions and disorders Integrates recent advancements of Artificial Intelligence to the evaluation of large amounts of clinical data for the early detection of disorders such as Epilepsy, Alcoholism, Sleep Apnea, motor-imagery tasks classification, and others Includes illustrative examples on how Artificial Intelligence can be applied to the Brain-Computer Interface, including a wide range of case studies in predicting and classification of neurological disorders

Author : Tuan Quang Dinh (Ph.D.)
Publisher :
Page : 0 pages
File Size : 10,98 MB
Release : 2023
Category :
ISBN :

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Utilizing pre-trained models for knowledge transfer, or adaptation, has gained widespread adoption in deep learning tasks, owing to its superior efficiency and effectiveness compared to traditional training from scratch. As model sizes continue to expand, freezing pre-trained models has emerged as a viable practice for knowledge transfer, improving data and storage efficiency while mitigating the long-standing issue of catastrophic forgetting. This thesis investigates the potential for solving novel machine learning tasks by assembling frozen pre-trained models into a modular neural network. We employ proficient pre-trained models as building blocks of the modular network, examining various assembly strategies to optimize task performance while preserving inherent efficiency. Our findings demonstrate that this framework can deliver highly effective and efficient solutions across diverse learning contexts. In the sub-task adaptation setting, we propose a method called InRep+, designed to reprogram frozen unconditional generators for conditional generation. This approach achieves high-performance generation while exhibiting robustness against imbalanced and noisy supervision. For cross-modal adaptation, our language-interfaced adaptation procedure enables large pre-trained language models to excel in non-language tasks without any architectural modifications. Moreover, we show that frozen language-image pre-trained models can be effectively and efficiently used for composing visual and topological word similarities, creating a robust unsupervised word translation system. Lastly, we propose modular ensemble methods to augment pre-trained code language models in correcting potentially buggy code, an area where single models fail dramatically. This thesis stands as a pioneering contribution to the comprehension and development of methodologies for constructing modular deep neural network systems utilizing pre-trained models.

Author : Kohei Arai
Publisher : Springer Nature
Page : 858 pages
File Size : 16,55 MB
Release : 2021-08-02
Category : Technology & Engineering
ISBN : 303082196X

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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.