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This book addresses one of the most important unsolved problems in
artificial intelligence: the task of learning, in an unsupervised
manner, from massive quantities of spatiotemporal visual data that
are available at low cost. The book covers important scientific
discoveries and findings, with a focus on the latest advances in
the field. Presenting a coherent structure, the book logically
connects novel mathematical formulations and efficient
computational solutions for a range of unsupervised learning tasks,
including visual feature matching, learning and classification,
object discovery, and semantic segmentation in video. The final
part of the book proposes a general strategy for visual learning
over several generations of student-teacher neural networks, along
with a unique view on the future of unsupervised learning in
real-world contexts. Offering a fresh approach to this difficult
problem, several efficient, state-of-the-art unsupervised learning
algorithms are reviewed in detail, complete with an analysis of
their performance on various tasks, datasets, and experimental
setups. By highlighting the interconnections between these methods,
many seemingly diverse problems are elegantly brought together in a
unified way. Serving as an invaluable guide to the computational
tools and algorithms required to tackle the exciting challenges in
the field, this book is a must-read for graduate students seeking a
greater understanding of unsupervised learning, as well as
researchers in computer vision, machine learning, robotics, and
related disciplines.
This book addresses one of the most important unsolved problems in
artificial intelligence: the task of learning, in an unsupervised
manner, from massive quantities of spatiotemporal visual data that
are available at low cost. The book covers important scientific
discoveries and findings, with a focus on the latest advances in
the field. Presenting a coherent structure, the book logically
connects novel mathematical formulations and efficient
computational solutions for a range of unsupervised learning tasks,
including visual feature matching, learning and classification,
object discovery, and semantic segmentation in video. The final
part of the book proposes a general strategy for visual learning
over several generations of student-teacher neural networks, along
with a unique view on the future of unsupervised learning in
real-world contexts. Offering a fresh approach to this difficult
problem, several efficient, state-of-the-art unsupervised learning
algorithms are reviewed in detail, complete with an analysis of
their performance on various tasks, datasets, and experimental
setups. By highlighting the interconnections between these methods,
many seemingly diverse problems are elegantly brought together in a
unified way. Serving as an invaluable guide to the computational
tools and algorithms required to tackle the exciting challenges in
the field, this book is a must-read for graduate students seeking a
greater understanding of unsupervised learning, as well as
researchers in computer vision, machine learning, robotics, and
related disciplines.
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