Gu Hongyang (17 results)

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Hardcover. Condition: new. Hardcover. This book delves into the cutting-edge field of person re-identification (ReID), a critical area within deep learning and computer vision. Addressing key challenges in current ReID models, it presents novel research combining automated machine learning (AutoML) techniques across three core aspects: data augmentation, network architecture, and loss functions.Readers will discover five innovative methods designed to overcome specific limitations in existing ReID systems. These include automated erasing data augmentation for more effective erased regions, two distinct AutoML approaches for optimizing multi-scale features in both single-branch and multi-branch architectures, and dynamic and static search methods for refining margin-based Softmax losses. The book's strength lies in its detailed exposition of search algorithms, regularization techniques, and reinforcement learning applications, all contributing to highly efficient and performant ReID solutions.The primary value of this book for readers lies in its comprehensive overview of advanced AutoML strategies tailored for ReID, offering practical insights into developing more robust and accurate models. It provides a structured exploration of complex concepts, empowering researchers and practitioners to push the boundaries of their own work. This book is an essential resource for researchers, graduate students, and engineers in computer vision, machine learning, and artificial intelligence, particularly those focused on person re-identification and automated deep learning. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.

Condition: New.

Condition: New.

Condition: As New. Unread book in perfect condition.

Condition: As New. Unread book in perfect condition.

Condition: New.

Hardcover. Condition: Brand New. 160 pages. 9.25x6.10x9.21 inches. In Stock.

Condition: New.

Buch. Condition: Neu. Druck auf Anfrage Neuware - Printed after ordering - This book delves into the cutting-edge field of person re-identification (ReID), a critical area within deep learning and computer vision. Addressing key challenges in current ReID models, it presents novel research combining automated machine learning (AutoML) techniques across three core aspects: data augmentation, network architecture, and loss functions.Readers will discover five innovative methods designed to overcome specific limitations in existing ReID systems. These include automated erasing data augmentation for more effective erased regions, two distinct AutoML approaches for optimizing multi-scale features in both single-branch and multi-branch architectures, and dynamic and static search methods for refining margin-based Softmax losses. The book's strength lies in its detailed exposition of search algorithms, regularization techniques, and reinforcement learning applications, all contributing to highly efficient and performant ReID solutions.The primary value of this book for readers lies in its comprehensive overview of advanced AutoML strategies tailored for ReID, offering practical insights into developing more robust and accurate models. It provides a structured exploration of complex concepts, empowering researchers and practitioners to push the boundaries of their own work. This book is an essential resource for researchers, graduate students, and engineers in computer vision, machine learning, and artificial intelligence, particularly those focused on person re-identification and automated deep learning.

Hardcover. Condition: Brand New. 160 pages. 9.25x6.10x9.21 inches. In Stock. This item is printed on demand.

Buch. Condition: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -This book delves into the cutting-edge field of person re-identification (ReID), a critical area within deep learning and computer vision. Addressing key challenges in current ReID models, it presents novel research combining automated machine learning (AutoML) techniques across three core aspects: data augmentation, network architecture, and loss functions.Readers will discover five innovative methods designed to overcome specific limitations in existing ReID systems. These include automated erasing data augmentation for more effective erased regions, two distinct AutoML approaches for optimizing multi-scale features in both single-branch and multi-branch architectures, and dynamic and static search methods for refining margin-based Softmax losses. The book's strength lies in its detailed exposition of search algorithms, regularization techniques, and reinforcement learning applications, all contributing to highly efficient and performant ReID solutions.The primary value of this book for readers lies in its comprehensive overview of advanced AutoML strategies tailored for ReID, offering practical insights into developing more robust and accurate models. It provides a structured exploration of complex concepts, empowering researchers and practitioners to push the boundaries of their own work. This book is an essential resource for researchers, graduate students, and engineers in computer vision, machine learning, and artificial intelligence, particularly those focused on person re-identification and automated deep learning. 184 pp. Englisch.

Hardcover. Condition: new. Hardcover. This book delves into the cutting-edge field of person re-identification (ReID), a critical area within deep learning and computer vision. Addressing key challenges in current ReID models, it presents novel research combining automated machine learning (AutoML) techniques across three core aspects: data augmentation, network architecture, and loss functions.Readers will discover five innovative methods designed to overcome specific limitations in existing ReID systems. These include automated erasing data augmentation for more effective erased regions, two distinct AutoML approaches for optimizing multi-scale features in both single-branch and multi-branch architectures, and dynamic and static search methods for refining margin-based Softmax losses. The book's strength lies in its detailed exposition of search algorithms, regularization techniques, and reinforcement learning applications, all contributing to highly efficient and performant ReID solutions.The primary value of this book for readers lies in its comprehensive overview of advanced AutoML strategies tailored for ReID, offering practical insights into developing more robust and accurate models. It provides a structured exploration of complex concepts, empowering researchers and practitioners to push the boundaries of their own work. This book is an essential resource for researchers, graduate students, and engineers in computer vision, machine learning, and artificial intelligence, particularly those focused on person re-identification and automated deep learning. This item is printed on demand. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability.

Buch. Condition: Neu. This item is printed on demand - Print on Demand Titel. Neuware -This book delves into the cutting-edge field of person re-identification (ReID), a critical area within deep learning and computer vision. Addressing key challenges in current ReID models, it presents novel research combining automated machine learning (AutoML) techniques across three core aspects: data augmentation, network architecture, and loss functions.Readers will discover five innovative methods designed to overcome specific limitations in existing ReID systems. These include automated erasing data augmentation for more effective erased regions, two distinct AutoML approaches for optimizing multi-scale features in both single-branch and multi-branch architectures, and dynamic and static search methods for refining margin-based Softmax losses. The book's strength lies in its detailed exposition of search algorithms, regularization techniques, and reinforcement learning applications, all contributing to highly efficient and performant ReID solutions.The primary value of this book for readers lies in its comprehensive overview of advanced AutoML strategies tailored for ReID, offering practical insights into developing more robust and accurate models. It provides a structured exploration of complex concepts, empowering researchers and practitioners to push the boundaries of their own work. This book is an essential resource for researchers, graduate students, and engineers in computer vision, machine learning, and artificial intelligence, particularly those focused on person re-identification and automated deep learning.Springer-Verlag KG, Sachsenplatz 4-6, 1201 Wien 196 pp. Englisch.

Condition: New. Print on Demand.

Condition: New. PRINT ON DEMAND.

Hardcover. Condition: new. Hardcover. This book delves into the cutting-edge field of person re-identification (ReID), a critical area within deep learning and computer vision. Addressing key challenges in current ReID models, it presents novel research combining automated machine learning (AutoML) techniques across three core aspects: data augmentation, network architecture, and loss functions.Readers will discover five innovative methods designed to overcome specific limitations in existing ReID systems. These include automated erasing data augmentation for more effective erased regions, two distinct AutoML approaches for optimizing multi-scale features in both single-branch and multi-branch architectures, and dynamic and static search methods for refining margin-based Softmax losses. The book's strength lies in its detailed exposition of search algorithms, regularization techniques, and reinforcement learning applications, all contributing to highly efficient and performant ReID solutions.The primary value of this book for readers lies in its comprehensive overview of advanced AutoML strategies tailored for ReID, offering practical insights into developing more robust and accurate models. It provides a structured exploration of complex concepts, empowering researchers and practitioners to push the boundaries of their own work. This book is an essential resource for researchers, graduate students, and engineers in computer vision, machine learning, and artificial intelligence, particularly those focused on person re-identification and automated deep learning. This item is printed on demand. Shipping may be from our Sydney, NSW warehouse or from our UK or US warehouse, depending on stock availability.