PyCIL: A Python Toolbox for Class-Incremental Learning
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...
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| Published in | arXiv.org |
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| Main Authors | , , , |
| Format | Paper Journal Article |
| Language | English |
| Published |
Ithaca
Cornell University Library, arXiv.org
26.10.2022
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| Online Access | Get full text |
| ISSN | 2331-8422 |
| DOI | 10.48550/arxiv.2112.12533 |
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| 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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| AbstractList | 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 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 |
| Author | Han-Jia, Ye Da-Wei, Zhou De-Chuan, Zhan Fu-Yun, Wang |
| Author_xml | – sequence: 1 givenname: Da-Wei surname: Zhou fullname: Zhou, Da-Wei – sequence: 2 givenname: Fu-Yun surname: Wang fullname: Wang, Fu-Yun – sequence: 3 givenname: Han-Jia surname: Ye fullname: Ye, Han-Jia – sequence: 4 givenname: De-Chuan surname: Zhan fullname: Zhan, De-Chuan |
| BackLink | https://doi.org/10.1007/s11432-022-3600-y$$DView published paper (Access to full text may be restricted) https://doi.org/10.48550/arXiv.2112.12533$$DView paper in arXiv |
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| Snippet | Traditional machine learning systems are deployed under the closed-world setting, which requires the entire training data before the offline training process.... Traditional machine learning systems are deployed under the closed-world setting, which requires the entire training data before the offline training process.... |
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| Title | PyCIL: A Python Toolbox for Class-Incremental Learning |
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