Central Library

Visual Domain Adaptation in the Deep Learning Era (Record no. 85566)

000 -LEADER
fixed length control field 04125nam a22005535i 4500
001 - CONTROL NUMBER
control field 978-3-031-79175-8
005 - DATE AND TIME OF LATEST TRANSACTION
control field 20240730164322.0
008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION
fixed length control field 220606s2022 sz | s |||| 0|eng d
020 ## - INTERNATIONAL STANDARD BOOK NUMBER
ISBN 9783031791758
-- 978-3-031-79175-8
082 04 - CLASSIFICATION NUMBER
Call Number 006
100 1# - AUTHOR NAME
Author Csurka, Gabriela.
245 10 - TITLE STATEMENT
Title Visual Domain Adaptation in the Deep Learning Era
250 ## - EDITION STATEMENT
Edition statement 1st ed. 2022.
300 ## - PHYSICAL DESCRIPTION
Number of Pages IV, 190 p.
490 1# - SERIES STATEMENT
Series statement Synthesis Lectures on Computer Vision,
520 ## - SUMMARY, ETC.
Summary, etc Solving problems with deep neural networks typically relies on massive amounts of labeled training data to achieve high performance. While in many situations huge volumes of unlabeled data can be and often are generated and available, the cost of acquiring data labels remains high. Transfer learning (TL), and in particular domain adaptation (DA), has emerged as an effective solution to overcome the burden of annotation, exploiting the unlabeled data available from the target domain together with labeled data or pre-trained models from similar, yet different source domains. The aim of this book is to provide an overview of such DA/TL methods applied to computer vision, a field whose popularity has increased significantly in the last few years. We set the stage by revisiting the theoretical background and some of the historical shallow methods before discussing and comparing different domain adaptation strategies that exploit deep architectures for visual recognition. We introduce the space of self-training-based methods that draw inspiration from the related fields of deep semi-supervised and self-supervised learning in solving the deep domain adaptation. Going beyond the classic domain adaptation problem, we then explore the rich space of problem settings that arise when applying domain adaptation in practice such as partial or open-set DA, where source and target data categories do not fully overlap, continuous DA where the target data comes as a stream, and so on. We next consider the least restrictive setting of domain generalization (DG), as an extreme case where neither labeled nor unlabeled target data are available during training. Finally, we close by considering the emerging area of learning-to-learn and how it can be applied to further improve existing approaches to cross domain learning problems such as DA and DG.
650 #0 - SUBJECT ADDED ENTRY--SUBJECT 1
General subdivision Digital techniques.
700 1# - AUTHOR 2
Author 2 Hospedales, Timothy M.
700 1# - AUTHOR 2
Author 2 Salzmann, Mathieu.
700 1# - AUTHOR 2
Author 2 Tommasi, Tatiana.
856 40 - ELECTRONIC LOCATION AND ACCESS
Uniform Resource Identifier https://doi.org/10.1007/978-3-031-79175-8
942 ## - ADDED ENTRY ELEMENTS (KOHA)
Koha item type eBooks
264 #1 -
-- Cham :
-- Springer International Publishing :
-- Imprint: Springer,
-- 2022.
336 ## -
-- text
-- txt
-- rdacontent
337 ## -
-- computer
-- c
-- rdamedia
338 ## -
-- online resource
-- cr
-- rdacarrier
347 ## -
-- text file
-- PDF
-- rda
650 #0 - SUBJECT ADDED ENTRY--SUBJECT 1
-- Image processing
650 #0 - SUBJECT ADDED ENTRY--SUBJECT 1
-- Computer vision.
650 #0 - SUBJECT ADDED ENTRY--SUBJECT 1
-- Pattern recognition systems.
650 14 - SUBJECT ADDED ENTRY--SUBJECT 1
-- Computer Imaging, Vision, Pattern Recognition and Graphics.
650 24 - SUBJECT ADDED ENTRY--SUBJECT 1
-- Computer Vision.
650 24 - SUBJECT ADDED ENTRY--SUBJECT 1
-- Automated Pattern Recognition.
830 #0 - SERIES ADDED ENTRY--UNIFORM TITLE
-- 2153-1064
912 ## -
-- ZDB-2-SXSC

No items available.