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Domain Adaptation for Visual Understanding (Hardcover, 1st ed. 2020)
Loot Price: R2,789
Discovery Miles 27 890
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Domain Adaptation for Visual Understanding (Hardcover, 1st ed. 2020)
Expected to ship within 10 - 15 working days
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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.
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