English
 
Help Privacy Policy Disclaimer
  Advanced SearchBrowse

Item

ITEM ACTIONSEXPORT
  Semantic Bottlenecks: Quantifying and Improving Inspectability of Deep Representations

Losch, M., Fritz, M., & Schiele, B. (2021). Semantic Bottlenecks: Quantifying and Improving Inspectability of Deep Representations. International Journal of Computer Vision, 129, 3136-3153. doi:10.1007/s11263-021-01498-0.

Item is

Basic

show hide
Genre: Journal Article
Latex : Semantic Bottlenecks: {Q}uantifying and Improving Inspectability of Deep Representations

Files

show Files
hide Files
:
Losch2021_Article_SemanticBottlenecksQuantifying.pdf (Publisher version), 7MB
Name:
Losch2021_Article_SemanticBottlenecksQuantifying.pdf
Description:
-
OA-Status:
Visibility:
Public
MIME-Type / Checksum:
application/pdf / [MD5]
Technical Metadata:
Copyright Date:
-
Copyright Info:
Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder.

Locators

show

Creators

show
hide
 Creators:
Losch, Max1, Author           
Fritz, Mario2, Author           
Schiele, Bernt1, Author           
Affiliations:
1Computer Vision and Machine Learning, MPI for Informatics, Max Planck Society, ou_1116547              
2External Organizations, ou_persistent22              

Content

show

Details

show
hide
Language(s): eng - English
 Dates: 20212021
 Publication Status: Published in print
 Pages: -
 Publishing info: -
 Table of Contents: -
 Rev. Type: -
 Identifiers: BibTex Citekey: Losch2021
DOI: 10.1007/s11263-021-01498-0
 Degree: -

Event

show

Legal Case

show

Project information

show

Source 1

show
hide
Title: International Journal of Computer Vision
  Other : Int. J. Comput. Vis.
Source Genre: Journal
 Creator(s):
Affiliations:
Publ. Info: New York, NY : Springer
Pages: - Volume / Issue: 129 Sequence Number: - Start / End Page: 3136 - 3153 Identifier: ISSN: 0920-5691
CoNE: https://pure.mpg.de/cone/journals/resource/954925564668