Domain Adaptation for Visual Understanding
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 se
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daptation for Visual Understanding
Domain Adaptation for Visual Understanding
Richa Singh Mayank Vatsa Vishal M. Patel Nalini Ratha •
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Editors
Domain Adaptation for Visual Understanding
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Editors Richa Singh Indraprastha Institute of Information Technology Delhi New Delhi, India
Mayank Vatsa Indraprastha Institute of Information Technology Delhi New Delhi, India
Vishal M. Patel Johns Hopkins University Baltimore, MD, USA
Nalini Ratha IBM Thomas J. Watson Research Center Yorktown Heights, NY, USA
ISBN 978-3-030-30670-0 ISBN 978-3-030-30671-7 https://doi.org/10.1007/978-3-030-30671-7
(eBook)
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Preface
In many real-world vision applications, there are very few or even no labeled samples, while an unrelated general domain is often available with a large number of labeled examples. For example, ImageNet contains millions of loosely labeled images over a large number of general classes of objects. On the other hand, a medical researcher may be interested in retrieving brain cancer fMRI scans closer to the patient’s brain scan image. Such data may not be available in large volumes or may be expensive to put forth the effort to annotate their collections by themselves. The problem of a lack of training samples can be challenging because of the significant statistical distribution difference between the feature distributions of training samples from the known available domain and the application domain. Researchers have often resorted to many techniques such as fine-tuning, hard mining, transfer learning, and domain adaptation to effectively use the large training samples from one domain and still get
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