English
 
Help Privacy Policy Disclaimer
  Advanced SearchBrowse

Item

ITEM ACTIONSEXPORT

Released

Conference Paper

Semantic Bottlenecks: Quantifying & Improving Inspectability of Deep Representations

MPS-Authors
/persons/resource/persons243315

Losch,  Max
Computer Vision and Machine Learning, MPI for Informatics, Max Planck Society;

/persons/resource/persons45383

Schiele,  Bernt       
Computer Vision and Machine Learning, MPI for Informatics, Max Planck Society;

External Resource
No external resources are shared
Fulltext (restricted access)
There are currently no full texts shared for your IP range.
Fulltext (public)
There are no public fulltexts stored in PuRe
Supplementary Material (public)
There is no public supplementary material available
Citation

Losch, M., Fritz, M., & Schiele, B. (2021). Semantic Bottlenecks: Quantifying & Improving Inspectability of Deep Representations. In Z. Akata, A. Geiger, & T. Sattler (Eds.), Pattern Recognition (pp. 15-29). Berlin: Springer.


Cite as: https://hdl.handle.net/21.11116/0000-0008-2792-7
Abstract
There is no abstract available