CL-gym: Full-featured pytorch library for continual learning

Seyed Iman Mirzadeh, Hassan Ghasemzadeh

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Continual learning (CL) has become one of the most active research venues within the artificial intelligence community in recent years. Given the significant amount of attention paid to continual learning, the need for a library that facilitates both research and development in this field is more visible than ever. However, CL algorithms' codes are currently scattered over isolated repositories written with different frameworks, making it difficult for researchers and practitioners to work with various CL algorithms and benchmarks using the same interface. In this paper, we introduce CL-Gym, a full-featured continual learning library that overcomes this challenge and accelerates the research and development. In addition to the necessary infrastructure for running end-to-end continual learning experiments, CL-Gym includes benchmarks for various CL scenarios and several state-of-the-art CL algorithms. In this paper, we present the architecture, design philosophies, and technical details behind CL-Gym 1.

Original languageEnglish (US)
Title of host publicationProceedings - 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2021
PublisherIEEE Computer Society
Pages3616-3622
Number of pages7
ISBN (Electronic)9781665448994
DOIs
StatePublished - Jun 2021
Externally publishedYes
Event2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2021 - Virtual, Online, United States
Duration: Jun 19 2021Jun 25 2021

Publication series

NameIEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops
ISSN (Print)2160-7508
ISSN (Electronic)2160-7516

Conference

Conference2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2021
Country/TerritoryUnited States
CityVirtual, Online
Period6/19/216/25/21

ASJC Scopus subject areas

  • Computer Vision and Pattern Recognition
  • Electrical and Electronic Engineering

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