I agree that this site is using cookies. You can find further informations
here
.
X
Login
Merkliste (
0
)
Home
About us
Home About us
Our history
Profile
Press & public relations
Friends
The library in figures
Exhibitions
Projects
Training, internships, careers
Films
Services & Information
Home Services & Information
Lending and interlibrary loans
Returns and renewals
Training and library tours
My Account
Library cards
New to the library?
Download Information
Opening hours
Learning spaces
PC, WLAN, copy, scan and print
Catalogs and collections
Home Catalogs and Collections
Rare books and manuscripts
Digital collections
Subject Areas
Our sites
Home Our sites
Central Library
Law Library (Juridicum)
BB Business and Economics (BB11)
BB Physics and Electrical Engineering
TB Engineering and Social Sciences
TB Economics and Nautical Sciences
TB Music
TB Art & Design
TB Bremerhaven
Contact the library
Home Contact the library
Staff Directory
Open access & publishing
Home Open access & publishing
Reference management: Citavi & RefWorks
Publishing documents
Open Access in Bremen
zur Desktop-Version
Toggle navigation
Merkliste
1 Ergebnisse
1
Semi-supervised Active Learning for Semi-supervised Models:..:
, In:
2021 IEEE/CVF International Conference on Computer Vision (ICCV)
,
Guo, Jiannan
;
Shi, Haochen
;
Kang, Yangyang
... - p. 2876-2885 , 2021
Link:
https://doi.org/10.1109/ICCV48922.2021.00289
RT T1
2021 IEEE/CVF International Conference on Computer Vision (ICCV)
: T1
Semi-supervised Active Learning for Semi-supervised Models: Exploit Adversarial Examples with Graph-based Virtual Labels
UL https://suche.suub.uni-bremen.de/peid=ieee-9709988&Exemplar=1&LAN=DE A1 Guo, Jiannan A1 Shi, Haochen A1 Kang, Yangyang A1 Kuang, Kun A1 Tang, Siliang A1 Jiang, Zhuoren A1 Sun, Changlong A1 Wu, Fei A1 Zhuang, Yueting YR 2021 SN 2380-7504 K1 Computer vision K1 Costs K1 Computational modeling K1 Clustering algorithms K1 Semisupervised learning K1 Rendering (computer graphics) K1 Data models K1 Optimization and learning methods; Datasets and evaluation; Machine learning architectures and formulations; Representation learning; Transfer/Low-shot/Semi/Unsupervised Learning SP 2876 OP 2885 LK http://dx.doi.org/https://doi.org/10.1109/ICCV48922.2021.00289 DO https://doi.org/10.1109/ICCV48922.2021.00289 SF ELIB - SuUB Bremen
Export
RefWorks (nur Desktop-Version!)
Flow
(Zuerst in
Flow
einloggen, dann importieren)