English
 
Help Privacy Policy Disclaimer
  Advanced SearchBrowse

Item

ITEM ACTIONSEXPORT
Add to Basket
  Local TagsRelease HistoryDetailsSummary

Released
Conference Paper

GS-WGAN: A Gradient-Sanitized Approach for Learning Differentially Private Generators

MPS-Authors
/persons/resource/persons204096

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

/persons/resource/persons44451

Fritz,  Mario
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

Chen, D., Orekondy, T., & Fritz, M. (2020). GS-WGAN: A Gradient-Sanitized Approach for Learning Differentially Private Generators. In H. Larochelle, M. Ranzato, R. Hadsell, M. F. Balcan, & H. Lin (Eds.), Advances in Neural Information Processing Systems 33 (pp. 12673-12684). Curran Associates, Inc.


Cite as: https://hdl.handle.net/21.11116/0000-0008-1866-B
Abstract
There is no abstract available