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  RMM: Reinforced Memory Management for Class-Incremental Learning

Liu, Y., Schiele, B., & Sun, Q. (2021). RMM: Reinforced Memory Management for Class-Incremental Learning. In M. Ranzato, A. Beygelzimer, P. S. Liang, J. W. Vaughan, & Y. Dauphin (Eds.), Advances in Neural Information Processing Systems 34 (pp. 3478-3490). Curran Associates, Inc.

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Item Permalink: https://hdl.handle.net/21.11116/0000-0009-A78D-C Version Permalink: https://hdl.handle.net/21.11116/0000-000E-219E-B
Genre: Conference Paper
Latex : {RMM}: {R}einforced Memory Management for Class-Incremental Learning

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NeurIPS-2021-rmm-reinforced-memory-management-for-class-incremental-learning-Paper.pdf (Preprint), 852KB
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 Creators:
Liu, Yaoyao1, Author           
Schiele, Bernt1, Author                 
Sun, Qianru2, Author           
Affiliations:
1Computer Vision and Machine Learning, MPI for Informatics, Max Planck Society, ou_1116547              
2External Organizations, ou_persistent22              

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Language(s): eng - English
 Dates: 2021-10-252021
 Publication Status: Published online
 Pages: 17 p.
 Publishing info: -
 Table of Contents: -
 Rev. Type: -
 Identifiers: BibTex Citekey: Liu_Neurips2021
 Degree: -

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Title: 35th Conference on Neural Information Processing Systems
Place of Event: Virtual
Start-/End Date: 2021-12-07 - 2021-12-07

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Title: Advances in Neural Information Processing Systems 34
  Abbreviation : NeurIPS 2021
  Other : 35th Conference on Neural Information Processing Systems
Source Genre: Proceedings
 Creator(s):
Ranzato, M.1, Editor
Beygelzimer, A.1, Editor
Liang, P. S.1, Editor
Vaughan, J. W.1, Editor
Dauphin, Y.1, Editor
Affiliations:
1 External Organizations, ou_persistent22            
Publ. Info: Curran Associates, Inc.
Pages: 13 p. Volume / Issue: - Sequence Number: - Start / End Page: 3478 - 3490 Identifier: -