PyCIL: A Python Toolbox for Class-Incremental Learning

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

Accepted to SCIENCE CHINA Information Sciences. Code is available at https://github.com/G-U-N/PyCIL

Cited by in corpus (5)

• Class-Incremental Learning: A Survey
• Towards Continual Egocentric Activity Recognition: A Multi-modal Egocentric Activity Dataset for Continual Learning
• Continual Learning with Neuromorphic Computing: Foundations, Methods, and Emerging Applications
• Future-Proofing Class-Incremental Learning
• REAL: Representation Enhanced Analytic Learning for Exemplar-free Class-incremental Learning