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Author: Howard Weinstein Publisher: Simon and Schuster ISBN: 0743419847 Category : Fiction Languages : en Pages : 434
Book Description
Deep Domain A routine diplomatic visit to the water-world of Akkalla becomes a nightmarish search for a missing Spock and Chekov, a search that plunges Admiral Kirk headlong into a corrupt government's desperate struggle to retain power. For both A Federation Science outpost and Akkalla's valiant freedom fighters have begun uncovering the ancient secrets hidden beneath her tranquil oceans. Secrets whose exposure may even mean civil war for the people of Akkalla -- and death for the crew of the Starship Enterprise™.
Author: Howard Weinstein Publisher: Simon and Schuster ISBN: 0743419847 Category : Fiction Languages : en Pages : 434
Book Description
Deep Domain A routine diplomatic visit to the water-world of Akkalla becomes a nightmarish search for a missing Spock and Chekov, a search that plunges Admiral Kirk headlong into a corrupt government's desperate struggle to retain power. For both A Federation Science outpost and Akkalla's valiant freedom fighters have begun uncovering the ancient secrets hidden beneath her tranquil oceans. Secrets whose exposure may even mean civil war for the people of Akkalla -- and death for the crew of the Starship Enterprise™.
Author: Howard Weinstein Publisher: Star Trek ISBN: 9780671705497 Category : Missing persons Languages : en Pages : 0
Book Description
A routine diplomatic visit to the water-world of Akkalla becomes a nightmarish search for a missing Spock and Chekov. The search plunges Admiral Kirk into a corrupt government's desperate struggle to retain power.
Author: Gustau Camps-Valls Publisher: John Wiley & Sons ISBN: 1119646162 Category : Technology & Engineering Languages : en Pages : 436
Book Description
DEEP LEARNING FOR THE EARTH SCIENCES Explore this insightful treatment of deep learning in the field of earth sciences, from four leading voices Deep learning is a fundamental technique in modern Artificial Intelligence and is being applied to disciplines across the scientific spectrum; earth science is no exception. Yet, the link between deep learning and Earth sciences has only recently entered academic curricula and thus has not yet proliferated. Deep Learning for the Earth Sciences delivers a unique perspective and treatment of the concepts, skills, and practices necessary to quickly become familiar with the application of deep learning techniques to the Earth sciences. The book prepares readers to be ready to use the technologies and principles described in their own research. The distinguished editors have also included resources that explain and provide new ideas and recommendations for new research especially useful to those involved in advanced research education or those seeking PhD thesis orientations. Readers will also benefit from the inclusion of: An introduction to deep learning for classification purposes, including advances in image segmentation and encoding priors, anomaly detection and target detection, and domain adaptation An exploration of learning representations and unsupervised deep learning, including deep learning image fusion, image retrieval, and matching and co-registration Practical discussions of regression, fitting, parameter retrieval, forecasting and interpolation An examination of physics-aware deep learning models, including emulation of complex codes and model parametrizations Perfect for PhD students and researchers in the fields of geosciences, image processing, remote sensing, electrical engineering and computer science, and machine learning, Deep Learning for the Earth Sciences will also earn a place in the libraries of machine learning and pattern recognition researchers, engineers, and scientists.
Author: Dominic Walliman Publisher: ISBN: 9781912497126 Category : Ocean Languages : en Pages : 69
Book Description
Where did the oceans come from? Can you take a submarine to the bottom of the sea? What exactly is a coral reef? Learn about ocean creatures big and small, and how humans explore the underwater world in this incredible illustrated book on the depths of the sea. Join your helpful guide, Professor Astro Cat, as he takes a dive from the seashore all the way to the ocean floor. From whales to deep-sea vents, there's so much to discover on this Deep-Sea Voyage.
Author: Gabriela Csurka Publisher: Morgan & Claypool Publishers ISBN: 163639342X Category : Computers Languages : en Pages : 190
Book Description
Solving problems with deep neural networks typically relies on massive amounts of labeled training data to achieve high performance/b>. 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: Hemanth Venkateswara Publisher: Springer Nature ISBN: 3030455297 Category : Computers Languages : en Pages : 256
Book Description
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 Publisher: Springer ISBN: 3319583476 Category : Computers Languages : en Pages : 338
Book Description
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 Publisher: Springer Nature ISBN: 3030306712 Category : Computers Languages : en Pages : 144
Book Description
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: Massimiliano Mancini Publisher: Sapienza Università Editrice ISBN: 8893772485 Category : Computers Languages : en Pages : 285
Book Description
Despite being the leading paradigm in computer vision, deep neural networks are inherently limited by the visual and semantic information contained in their training set. In this thesis, we aim to design deep models operating with previously unseen visual domains and semantic concepts. We first describe different solutions for generalizing to new visual domains, applying variants of normalization layers to multiple challenging settings e.g. where new domain data is not available but arrives online or is described by metadata. In the second part, we incorporate new semantic concepts into pretrained deep models. We propose specific solutions for different problems such as multi-task/incremental learning and open-world recognition. Finally, we merge the two challenges: given images of multiple domains and categories, can we recognize unseen concepts in unseen domains? We propose an approach that is the first, promising step, towards solving this problem. Winner of the Competition “Prize for PhD Thesis 2020” arranged by Sapienza University Press.
Author: Howard Weinstein
Author: Howard Weinstein
Author: Howard Weinstein
Author: Gustau Camps-Valls
Author: Dominic Walliman
Author: Gabriela Csurka
Author: Hemanth Venkateswara
Author: Gabriela Csurka
Author: Richa Singh
Author: Jingjing Li
Author: Massimiliano Mancini