Unsupervised Learning in Space and Time: A Modern Approach for Computer...
Master unsupervised learning in computer vision with "Unsupervised Learning in Space and Time: A Modern Approach for Computer Vision using Graph-based Techniques and Deep Neural Networks." This comprehensive hardcover guide by Marius Leordeanu delves into one of artificial intelligence's most significant challenges: learning from vast amounts of spatiotemporal visual data without supervision. Discover cutting-edge scientific findings and explore novel mathematical formulations and efficient computational solutions for tasks like visual feature matching, object discovery, and semantic segmentation in videos. The book also proposes a unique strategy for visual learning using student-teacher neural networks and offers insights into the future of unsupervised learning. It provides a unified approach to understanding diverse problems and equips graduate students and researchers in computer vision, machine learning, and robotics with essential tools and algorithms. Key Features: * Addresses unsupervised learning for spatiotemporal visual data. * Focuses on graph-based techniques and deep neural networks. * Covers visual feature matching, learning, classification, object discovery, and semantic segmentation. * Proposes a general strategy for visual learning over generations of neural networks. * Includes 136 b/w illustrations and 96 colour illustrations. Unsupervised Learning in Space and Time is an invaluable resource for anyone looking to tackle the complex challenges in the field of unsupervised learning and computer vision.
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Details:Unsupervised Learning in Space and Time: A Modern Approach for Computer...
Master unsupervised learning in computer vision with "Unsupervised Learning in Space and Time: A Modern Approach for Computer Vision using Graph-based Techniques and Deep Neural Networks." This comprehensive hardcover guide by Marius Leordeanu delves into one of artificial intelligence's most significant challenges: learning from vast amounts of spatiotemporal visual data without supervision. Discover cutting-edge scientific findings and explore novel mathematical formulations and efficient computational solutions for tasks like visual feature matching, object discovery, and semantic segmentation in videos. The book also proposes a unique strategy for visual learning using student-teacher neural networks and offers insights into the future of unsupervised learning. It provides a unified approach to understanding diverse problems and equips graduate students and researchers in computer vision, machine learning, and robotics with essential tools and algorithms. Key Features: * Addresses unsupervised learning for spatiotemporal visual data. * Focuses on graph-based techniques and deep neural networks. * Covers visual feature matching, learning, classification, object discovery, and semantic segmentation. * Proposes a general strategy for visual learning over generations of neural networks. * Includes 136 b/w illustrations and 96 colour illustrations. Unsupervised Learning in Space and Time is an invaluable resource for anyone looking to tackle the complex challenges in the field of unsupervised learning and computer vision.
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Master unsupervised learning in computer vision with "Unsupervised Learning in Space and Time: A Modern Approach for Computer Vision using Graph-based Techniques and Deep Neural Networks." This comprehensive hardcover guide by Marius Leordeanu delves into one of artificial intelligence's most significant challenges: learning from vast amounts of spatiotemporal visual data without supervision. Discover cutting-edge scientific findings and explore novel mathematical formulations and efficient computational solutions for tasks like visual feature matching, object discovery, and semantic segmentation in videos. The book also proposes a unique strategy for visual learning using student-teacher neural networks and offers insights into the future of unsupervised learning. It provides a unified approach to understanding diverse problems and equips graduate students and researchers in computer vision, machine learning, and robotics with essential tools and algorithms. Key Features: * Addresses unsupervised learning for spatiotemporal visual data. * Focuses on graph-based techniques and deep neural networks. * Covers visual feature matching, learning, classification, object discovery, and semantic segmentation. * Proposes a general strategy for visual learning over generations of neural networks. * Includes 136 b/w illustrations and 96 colour illustrations. Unsupervised Learning in Space and Time is an invaluable resource for anyone looking to tackle the complex challenges in the field of unsupervised learning and computer vision.
Product Specifications
General | |
|---|---|
format | hardcover |
Language | english |
series | advances in computer vision and pattern recognition ser. |
type | textbook |
Brand | Springer Nature Switzerland AG |
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