• Produktbild: Domain Adaptation in Computer Vision with Deep Learning
  • Produktbild: Domain Adaptation in Computer Vision with Deep Learning

Domain Adaptation in Computer Vision with Deep Learning

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Beschreibung

Produktdetails

Einband

Gebundene Ausgabe

Erscheinungsdatum

19.08.2020

Abbildungen

XI, 256 p. 76 illus., 55 illus. in color.

Herausgeber

Hemanth Venkateswara + weitere

Verlag

Springer

Seitenzahl

256

Maße (L/B/H)

24,1/16/2,1 cm

Gewicht

571 g

Auflage

20001 Auflage 1st edition 2020

Sprache

Englisch

ISBN

978-3-030-45528-6

Beschreibung

Portrait


Hemanth Venkateswara is an Assistant Research Professor at the School of Computing Informatics and Decision Systems Engineering at Arizona State University. He completed his PhD in machine learning and computer vision in 2017 from Arizona State University. Hemanth’s research interests include transfer learning, active learning, zero-shot learning, incremental learning and generative models using deep learning. His research explores knowledge transfer paradigms for deep neural networks that are challenging to train due to paucity of annotated data. Hemanth holds a bachelor’s degree in Physics and master’s degrees in Physics and Computer Science. Prior to his PhD, Hemanth worked as a senior software engineer at Alcatel-Lucent Technologies, India. Hemanth is a member of the IEEE and the ACM.



Sethuraman “Panch” Panchanathan leads the knowledge enterprise at Arizona State University, which advances research, innovation, strategic partnerships, entrepreneurship,global and economic development at ASU. He is the Director of the Center for Cognitive Ubiquitous Computing at ASU. Panchanathan’s research interests are in the areas of human-centered multimedia computing, haptic user interfaces, person-centered tools and ubiquitous computing technologies for enhancing the quality of life for individuals with disabilities, machine learning for multimedia applications, medical image processing, and media processor designs. Panchanathan has published more than 500 papers in refereed journals and conferences and has mentored nearly 150 graduate students, post-docs, research engineers and research scientists who occupy leading positions in academia and industry. He was the editor-in-chief of the IEEE Multimedia Magazine and is also an editor/associate editor of several international journals and transactions. Panchanathan was appointed by President Barack Obama to the U.S. National Science Board for a six-year term and was appointed by the U.S. Secretaryof Commerce to the National Advisory Council on Innovation and Entrepreneurship. In Dec 2019, Panchanathan was nominated as the Director for the National Science Foundation by President Donald Trump. Panchanathan is a fellow and Vice President for Strategic Initiatives and Membership of the National Academy of Inventors. In 2018, Panchanathan was appointed Arizona Governor Doug Ducey’s Senior Advisor for Science & Technology. Panchanathan is a Fellow of the NAI, American Association for the Advancement of Science (AAAS), the Canadian Academy of Engineering (CAE), the Institute of Electrical and Electronics Engineers (IEEE) and the Society of Optical Engineering (SPIE).

Produktdetails

Einband

Gebundene Ausgabe

Erscheinungsdatum

19.08.2020

Abbildungen

XI, 256 p. 76 illus., 55 illus. in color.

Herausgeber

Verlag

Springer

Seitenzahl

256

Maße (L/B/H)

24,1/16/2,1 cm

Gewicht

571 g

Auflage

20001 Auflage 1st edition 2020

Sprache

Englisch

ISBN

978-3-030-45528-6

Herstelleradresse

Springer-Verlag KG
Sachsenplatz 4-6
1201 Wien
AT

Email: ProductSafety@springernature.com

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  • Produktbild: Domain Adaptation in Computer Vision with Deep Learning
  • Produktbild: Domain Adaptation in Computer Vision with Deep Learning

  • Preface.- Part I: Introduction.- Chapter 1: Introduction to Domain Adaptation.- Chapter 2: Shallow Domain Adaptation.- Part II:  Domain Alignment in the Feature Space.- Chapter 3: d-SNE: Domain Adaptation using Stochastic Neighborhood Embedding.- Chapter 4: Deep Hashing Network for Unsupervised Domain Adaptation.- Chapter 5:  Re-weighted Adversarial Adaptation Network for Unsupervised Domain Adaptation.- Part III:  Domain Alignment in the Image Space.- Chapter 6: Unsupervised Domain Adaptation with Duplex Generative Adversarial Network.- Chapter 7: Domain Adaptation via Image to Image Translation.- Chapter 8:  Domain Adaptation via Image Style Transfer.- Part IV: Future Directions in Domain Adaptation.- Chapter 9: Towards Scalable Image Classifier Learning with Noisy Labels via Domain Adaptation.- Chapter 10: Adversarial Learning Approach for Open Set Domain Adaptation.- Chapter 11:  UniversalDomain Adaptation.- Chapter 12:  Multi-source Domain Adaptation by Deep CockTail Networks.- Chapter 13: Zero-Shot Task Transfer.