PyCIL: A Python Toolbox for Class-Incremental Learning

2021-12-23 · Da-Wei Zhou, Fu-Yun Wang, Han-Jia Ye, De-Chuan Zhan

Traditional machine learning systems are deployed under the closed-world setting, which requires the entire training data before the offline training process. However, real-world applications often face the incoming new classes, and a model should incorporate them continually. The learning paradigm is called Class-Incremental Learning (CIL). We propose a Python toolbox that implements several key algorithms for class-incremental learning to ease the burden of researchers in the machine learning community. The toolbox contains implementations of a number of founding works of CIL such as EWC and iCaRL, but also provides current state-of-the-art algorithms that can be used for conducting novel fundamental research. This toolbox, named PyCIL for Python Class-Incremental Learning, is available at https://github.com/G-U-N/PyCIL

📄 PDF Abstract BibTeX arXiv:2112.12533

Code (1)

g-u-n/pycil 공식 구현 pytorch

Tasks

BIG-bench Machine Learning class-incremental learning Class Incremental Learning Incremental Learning

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