NDLI: Common-specific feature learning for multi-source domain adaptation

Content Provider	IET Digital Library
Author	Niu, Chang Shang, Junyuan Zhou, Zhiheng Huang, Junchu Wang, Tianlei Li, Xiangwei
Abstract	Multi-source domain adaptation (MDA) aims to leverage knowledge from multiple source domains to improve the classification performance on target domains. Different degrees of distribution discrepancies between every two domains pose a huge challenge to MDA tasks. Most works focus on extracting features shared by all domains, which is critical but not enough to reduce distribution discrepancies. In this paper, we propose a method named as common-specific feature learning (CSFL). Constituting a framework of feature learning, CSFL explores a subspace where the combination of common and specific features makes learned representations comprehensive. Based on this framework, we conduct a metric learning method for learning a discriminative feature representation. Considering redundant information caused by source domains is likely to hurt the performance, we impose an effective low-rank constraint to remove the redundant information. Further, we adopt structure consistent constraint to preserve the local structure in each domain. CSFL has obtained about 1–5% improvement of mean accuracy, compared to the state-of-the-art shallow methods. Further, compared with 90.2% and 89.4% of the best baseline deep method, CSFL achieves mean accuracy of 90.8% and 89.7% on the Office-31 and ImageCLEF-DA datasets respectively. The encouraging results validate the effectiveness of our method.
Starting Page	4049
Ending Page	4058
Page Count	10
ISSN	17519659
Volume Number	14
e-ISSN	17519667
Issue Number	Issue 16, Dec (2020)
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/10.1049/iet-ipr.2019.1712
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/iet-ipr/14/16
Journal	IET Image Processing
Publisher Date	2020-12-04
Access Restriction	Open
Rights Holder	© The Institution of Engineering and Technology
Subject Keyword	Common Features Common-specific Feature Learning Computer Vision And Image Processing Technique CSFL Distribution Discrepancies Feature Extraction Image Classification Knowledge Engineering Technique Learning in AI Multiple Source Domain Multisource Domain Adaptation Method Multisource Domain Adaptation Task Statistics Target Domain
Content Type	Text
Resource Type	Article
Subject	Signal Processing Electrical and Electronic Engineering Computer Vision and Pattern Recognition Software

Sl.	Authority	Responsibilities	Communication Details
1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
5	Website/Portal (Helpdesk)	Queries regarding NDLI and its services	support@ndl.gov.in
6	Contents and Copyright Issues	Queries related to content curation and copyright issues	content@ndl.gov.in
7	National Digital Library of India Club (NDLI Club)	Queries related to NDLI Club formation, support, user awareness program, seminar/symposium, collaboration, social media, promotion, and outreach	clubsupport@ndl.gov.in
8	Digital Preservation Centre (DPC)	Assistance with digitizing and archiving copyright-free printed books	dpc@ndl.gov.in
9	IDR Setup or Support	Queries related to establishment and support of Institutional Digital Repository (IDR) and IDR workshops	idr@ndl.gov.in

Domain Adaptation Network Based on Autoencoder

Transfer metric learning for unsupervised domain adaptation

Regularised transfer learning for hyperspectral image classification

Domain-invariant adversarial learning with conditional distribution alignment for unsupervised domain adaptation

Adversarial auto-encoder for unsupervised deep domain adaptation

Underwater target classification at greater depths using deep neural network with joint multiple-domain feature

Hyperspectral Image Classification Based on Unsupervised Heterogeneous Domain Adaptation CycleGan

Learning Domain-Invariant and Discriminative Features for Homogeneous Unsupervised Domain Adaptation

Progressive learning with style transfer for distant domain adaptation

Common-specific feature learning for multi-source domain adaptation

Similar Documents

Domain Adaptation Network Based on Autoencoder

Transfer metric learning for unsupervised domain adaptation

Regularised transfer learning for hyperspectral image classification

Domain-invariant adversarial learning with conditional distribution alignment for unsupervised domain adaptation

Adversarial auto-encoder for unsupervised deep domain adaptation

Underwater target classification at greater depths using deep neural network with joint multiple-domain feature

Hyperspectral Image Classification Based on Unsupervised Heterogeneous Domain Adaptation CycleGan

Learning Domain-Invariant and Discriminative Features for Homogeneous Unsupervised Domain Adaptation

Progressive learning with style transfer for distant domain adaptation

Common-specific feature learning for multi-source domain adaptation