PyCIL: A Python Toolbox for Class-Incremental Learning

• Published in: arXiv.org
• Main Authors: Zhou, Da-Wei, Wang, Fu-Yun, Ye, Han-Jia, Zhan, De-Chuan
• Format: Paper Journal Article
• Language: English
• Published: Ithaca Cornell University Library, arXiv.org 26.10.2022
• Subjects: Algorithms; Computer Science - Computer Vision and Pattern Recognition; Computer Science - Learning; Machine learning; Training
• ISSN: 2331-8422
• DOI: 10.48550/arxiv.2112.12533

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