A Task-Aware Network for Multi-task Learning
Creating a model capable of learning new tasks without deteriorating its performance on the previously learned tasks has been a challenge of multi-task learning. Fine-tuning a pre-trained network for another task could change the network in a way that deg
- PDF / 32,986,011 Bytes
- 269 Pages / 439.37 x 666.142 pts Page_size
- 84 Downloads / 267 Views
		    ommunications in Computer and Information Science
 
 Urban Intelligence and Applications Second International Conference, ICUIA 2020 Taiyuan, China, August 14–16, 2020 Revised Selected Papers
 
 1319
 
 Communications in Computer and Information Science Editorial Board Members Joaquim Filipe Polytechnic Institute of Setúbal, Setúbal, Portugal Ashish Ghosh Indian Statistical Institute, Kolkata, India Raquel Oliveira Prates Federal University of Minas Gerais (UFMG), Belo Horizonte, Brazil Lizhu Zhou Tsinghua University, Beijing, China
 
 1319
 
 More information about this series at http://www.springer.com/series/7899
 
 Xiaohui Yuan Mohamed Elhoseny Jianfang Shi (Eds.) •
 
 •
 
 Urban Intelligence and Applications Second International Conference, ICUIA 2020 Taiyuan, China, August 14–16, 2020 Revised Selected Papers
 
 123
 
 Editors Xiaohui Yuan University of North Texas Denton, TX, USA
 
 Mohamed Elhoseny Mansoura University Mansoura, Egypt
 
 Jianfang Shi Taiyuan University of Technology Taiyuan, China
 
 ISSN 1865-0929 ISSN 1865-0937 (electronic) Communications in Computer and Information Science ISBN 978-981-33-4600-0 ISBN 978-981-33-4601-7 (eBook) https://doi.org/10.1007/978-981-33-4601-7 © Springer Nature Singapore Pte Ltd. 2020 This work is subject to copyright. All rights are reserved by the Publisher, whether the whole or part of the material is concerned, specifically the rights of translation, reprinting, reuse of illustrations, recitation, broadcasting, reproduction on microfilms or in any other physical way, and transmission or information storage and retrieval, electronic adaptation, computer software, or by similar or dissimilar methodology now known or hereafter developed. The use of general descriptive names, registered names, trademarks, service marks, etc. in this publication does not imply, even in the absence of a specific statement, that such names are exempt from the relevant protective laws and regulations and therefore free for general use. The publisher, the authors and the editors are safe to assume that the advice and information in this book are believed to be true and accurate at the date of publication. Neither the publisher nor the authors or the editors give a warranty, expressed or implied, with respect to the material contained herein or for any errors or omissions that may have been made. The publisher remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. This Springer imprint is published by the registered company Springer Nature Singapore Pte Ltd. The registered company address is: 152 Beach Road, #21-01/04 Gateway East, Singapore 189721, Singapore
 
 Preface
 
 The proceedings include papers presented at the International Conference on Urban Intelligence and Applications (ICUIA) held in Taiyuan, China, during August 14–16, 2020. This conference series provided an international forum to present, discuss, and exchange innovative ideas and recent developments in the fields of computer science, computational geography, and management. The proceedings provide new ad		
 
	 
	 
	 
	 
	 
	 
	 
	 
	 
	 
	