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PyCIL: a Python toolbox for class-incremental learning

delete2023-04-19
delete19
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OA
AI
D
Da-Wei Zhou
F
Fuyun Wang
叶
叶翰嘉 (Han-Jia Ye) *
D
De‐Chuan Zhan
DOI:10.1007/s11432-022-3600-ydelete
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Abstract

Abstract

En 中文
Conclusion We have presented PyCIL, a classincremental learning toolbox written in Python. It contains implementations of a number of founding studies of CIL, but also provides current state-of-the-art algorithms that can be used to conduct novel fundamental research. Code consistency makes it an easy tool for research purposes, teaching, and industrial applications.

Journal

Science China Information Sciences cover
Science China Information Sciences
IF:
7.6
Papers:
4.9K
Citations:
8.9K

Organization

N
nanjing university
Scholars:
7.8W
Papers: 5.6W
Citations: 87
Cited Papers

Cited Papers

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errShashank Ramesh Babu; Matias Jaskari; Antti Jarvenpää; Thomas Paul Davis; Jukka Kömi; David Porter
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Overcoming catastrophic forgetting in neural networks
err2017-03-14
err3.7K
errOAAI
errKirkpatricka, James; Pascanu, Razvan; Rabinowitz, Neil; Veness, Joel; Desjardins, Guillaume; Rusu, Andrei A.; Milan, Kieran; Quan, John; Ramalho, Tiago; Grabska-Barwinska, Agnieszka; Hassabis, Demis; Clopath, Claudia; Kumaran, Dharshan; Hadsell, Raia
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