English
 
Help Privacy Policy Disclaimer
  Advanced SearchBrowse

Item

ITEM ACTIONSEXPORT
 
 
DownloadE-Mail
 DetailsSummary
  Scalable Multitask Representation Learning for Scene Classification

Lapin, M., Schiele, B., & Hein, M. (2014). Scalable Multitask Representation Learning for Scene Classification. In 2014 IEEE Conference on Computer Vision and Pattern Recognition (pp. 1434-1441). Los Alamitos, CA: IEEE Computer Society. doi:10.1109/CVPR.2014.186.

Item is

 

show all hide all

Basic

show hide
Item Permalink: https://hdl.handle.net/11858/00-001M-0000-0019-8874-8 Version Permalink: https://hdl.handle.net/11858/00-001M-0000-0028-E16B-1
Genre: Conference Paper

Files

show Files
hide Files
:
lapin14scalable.pdf (Preprint), 261KB
 
File Permalink:
-
Name:
lapin14scalable.pdf
Description:
-
OA-Status:
Visibility:
Private
MIME-Type / Checksum:
application/pdf
Technical Metadata:
Copyright Date:
-
Copyright Info:
-
License:
-

Locators

show

Creators

show
hide
 Creators:
Lapin, Maksim1, Author           
Schiele, Bernt1, Author           
Hein, Matthias2, Author
Affiliations:
1Computer Vision and Multimodal Computing, MPI for Informatics, Max Planck Society, ou_1116547              
2External Organizations, ou_persistent22              

Content

show

Details

show
hide
Language(s): eng - English
 Dates: 2014-03-0120142014
 Publication Status: Issued
 Pages: -
 Publishing info: -
 Table of Contents: -
 Rev. Type: -
 Identifiers: BibTex Citekey: 860
DOI: 10.1109/CVPR.2014.186
 Degree: -

Event

show
hide
Title: 2014 IEEE Conference on Computer Vision and Pattern Recognition
Place of Event: Columbus, OH, USA
Start-/End Date: 2014-06-24 - 2014-06-27

Legal Case

show

Project information

show

Source 1

show
hide
Title: 2014 IEEE Conference on Computer Vision and Pattern Recognition
  Abbreviation : CVPR 2014
  Subtitle : Proceedings ; 23–28 June 2014, Columbus, Ohio
Source Genre: Proceedings
 Creator(s):
Affiliations:
Publ. Info: Los Alamitos, CA : IEEE Computer Society
Pages: - Volume / Issue: - Sequence Number: - Start / End Page: 1434 - 1441 Identifier: ISBN: 978-1-4799-5117-8