Overview
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; presentsa 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.
Full Product Details
Author: Richa Singh , Mayank Vatsa , Vishal M. Patel , Nalini Ratha
Publisher: Springer Nature Switzerland AG
Imprint: Springer Nature Switzerland AG
Edition: 1st ed. 2020
Weight: 0.454kg
ISBN: 9783030306700
ISBN 10: 3030306704
Pages: 144
Publication Date: 09 January 2020
Audience:
Professional and scholarly
,
Professional & Vocational
Format: Hardback
Publisher's Status: Active
Availability: Manufactured on demand 
We will order this item for you from a manufactured on demand supplier.
Table of Contents
Domain Adaptation for Visual Understanding Soumyadeep Ghosh, Richa Singh, Mayank Vatsa, Nalini Ratha, and Vishal M. Patel M-ADDA: Unsupervised Domain Adaptation with Deep Metric Learning Issam H. Laradji and Reza Babanezhad XGAN: Unsupervised Image-to-Image Translation for Many-to-Many Mappings Amélie Royer, Konstantinos Bousmalis, Stephan Gouws, Fred Bertsch, Inbar Mosseri, Forrester Cole, and Kevin Murphy Improving Transferability of Deep Neural Networks Parijat Dube, Bishwaranjan Bhattacharjee, Elisabeth Petit-Bois, and Matthew Hill Cross Modality Video Segment Retrieval with Ensemble Learning Xinyan Yu, Ya Zhang, and Rui Zhang On Minimum Discrepancy Estimation for Deep Domain Adaptation Mohammad Mahfujur Rahman, Clinton Fookes, Mahsa Baktashmotlagh, and Sridha Sridharan Multi-Modal Conditional Feature Enhancement for Facial Action Unit Recognition Nagashri N. Lakshminarayana, Deen Dayal Mohan, Nishant Sankaran, Srirangaraj Setlur, and Venu Govindaraju Intuition Learning Anush Sankaran, Mayank Vatsa, and Richa Singh Alleviating Tracking Model Degradation Using Interpolation-Based Progressive Updating Xiyu Kong, Qiping Zhou, Yunyu Lai, Muming Zhao, and Chongyang Zhang
Reviews
Author Information
Dr. Richa Singh is a Professor at Indraprastha Institute of Information Technology, Delhi, India. Dr. Mayank Vatsa is a Professor at the same institution. Dr. Vishal M. Patel is an Assistant Professor in the Department of Electrical and Computer Engineering at Johns Hopkins University, Baltimore, MD, USA. Dr. Nalini Ratha is a Research Staff Member at the IBM Thomas J. Watson Research Center, Yorktown Heights, NY, USA.
Tab Content 6
Author Website:
Customer Reviews
Recent Reviews
No review item found!
Add your own review!
Countries Available
All regions
|
