TY - GEN
T1 - CL-gym
T2 - 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2021
AU - Mirzadeh, Seyed Iman
AU - Ghasemzadeh, Hassan
N1 - Funding Information:
This work was supported in part by the United States National Science Foundation, under grant CNS-1750679. Any opinions, findings, conclusions, or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of the funding organizations. The authors thank Mehrdad Farajtabar and Anonymous Reviewers for their valuable comments and feedback.
Publisher Copyright:
© 2021 IEEE.
PY - 2021/6
Y1 - 2021/6
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=85116073125&partnerID=8YFLogxK
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U2 - 10.1109/CVPRW53098.2021.00401
DO - 10.1109/CVPRW53098.2021.00401
M3 - Conference contribution
AN - SCOPUS:85116073125
T3 - IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops
SP - 3616
EP - 3622
BT - Proceedings - 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2021
PB - IEEE Computer Society
Y2 - 19 June 2021 through 25 June 2021
ER -