PyCIL: A Python Toolbox for Class-Incremental Learning

• URL: http://arxiv.org/abs/2112.12533v1
• Date: Thu, 23 Dec 2021 13:41:24 GMT
• Title: PyCIL: A Python Toolbox for Class-Incremental Learning
• Authors: Da-Wei Zhou, Fu-Yun Wang, Han-Jia Ye, De-Chuan Zhan
• Abstract summary: We propose a Python toolbox that implements several key algorithms for class-incremental learning. The toolbox contains implementations of a number of founding works of CIL such as EWC and iCaRL. It also provides current state-of-the-art algorithms that can be used for conducting novel fundamental research.
• Score: 34.32500654158169
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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

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