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  Assessing Generative Models via Precision and Recall

Sajjadi, M., Bachem, O., Lucic, M., Bousquet, O., & Gelly, S. (2019). Assessing Generative Models via Precision and Recall. In S. Bengio, H. Wallach, H. Larochelle, K. Grauman, N. Cesa-Bianchi, & R. Garnett (Eds.), Advances in Neural Information Processing Systems 31 (pp. 5228-5237). Red Hook, NY: Curran Associates, Inc. Retrieved from https://papers.nips.cc/paper/7769-assessing-generative-models-via-precision-and-recall.

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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: 20182019-07
 Publication Status: Issued
 Pages: 10
 Publishing info: -
 Table of Contents: -
 Rev. Type: -
 Degree: -

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Title: 32nd Conference on Neural Information Processing Systems (NeurIPS 2018)
Place of Event: Montreal
Start-/End Date: 2018-12-02 - 2018-12-08

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Title: Advances in Neural Information Processing Systems 31
  Subtitle : 32nd Conference on Neural Information Processing Systems (NeurIPS 2018)
Source Genre: Proceedings
 Creator(s):
Bengio, S.1, Editor
Wallach , H.1, Editor
Larochelle, H.1, Editor
Grauman, K.1, Editor
Cesa-Bianchi, N.1, Editor
Garnett, R.1, Editor
Affiliations:
1 External Organizations, ou_persistent22            
Publ. Info: Red Hook, NY : Curran Associates, Inc.
Pages: - Volume / Issue: 8 Sequence Number: - Start / End Page: 5228 - 5237 Identifier: URI: https://papers.nips.cc/paper/2018
ISBN: 978-1-5108-8447-2