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  Assessing Generative Models via Precision and Recall

Sajjadi, M., Bachem, O., Lucic, M., Bousquet, O., & Gelly, S. (2018). Assessing Generative Models via Precision and Recall. In ICML 2018 Workshop on Theoretical Foundations and Applications of Deep Generative Models. Retrieved from https://sites.google.com/view/tadgm/accepted-papers.

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Genre: Conference Paper

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 Creators:
Sajjadi, M.S.M.1, Author           
Bachem, O.2, Author
Lucic, M.2, Author
Bousquet, O.2, Author
Gelly, S.2, Author
Affiliations:
1Dept. Empirical Inference, Max Planck Institute for Intelligent Systems, Max Planck Society, ou_1497647              
2External Organizations, ou_persistent22              

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Free keywords: Abt. Schölkopf
 Abstract: -

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Language(s): eng - English
 Dates: 2018
 Publication Status: Published online
 Pages: 14
 Publishing info: -
 Table of Contents: -
 Rev. Type: -
 Identifiers: BibTex Citekey: SajBahLucBouGel18b
URI: https://sites.google.com/view/tadgm/accepted-papers
 Degree: -

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Title: ICML 2018 Workshop on Theoretical Foundations and Applications of Deep Generative Models
Place of Event: Stockholm, Sweden
Start-/End Date: 2018-07-14 - 2018-07-15

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Title: ICML 2018 Workshop on Theoretical Foundations and Applications of Deep Generative Models
Source Genre: Proceedings
 Creator(s):
Affiliations:
Publ. Info: -
Pages: - Volume / Issue: - Sequence Number: - Start / End Page: - Identifier: URI: https://sites.google.com/view/tadgm/accepted-papers