continual-learning
PyTorch implementation of various methods for continual learning (XdG, EWC, SI, LwF, FROMP, DGR, BI-R, ER, A-GEM, iCaRL, Generative Classifier) in three different scenarios.
Conduct experiments on continual learning with deep neural networks, focusing on learning tasks sequentially over time.
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Free · no card · unsubscribe anytimePyTorch implementation of various methods for continual learning (XdG, EWC, SI, LwF, FROMP, DGR, BI-R, ER, A-GEM, iCaRL, Generative Classifier) in three different scenarios.
continual-learning has 1.9k stars on GitHub. It has been forked 346 times. continual-learning is written mainly in Jupyter Notebook. It has been in active development since 2018. continual-learning is available under the MIT license. Its main topics are artificial-neural-networks, class-incremental-learning, continual-learning, deep-learning.
What is continual-learning?
PyTorch implementation of various methods for continual learning (XdG, EWC, SI, LwF, FROMP, DGR, BI-R, ER, A-GEM, iCaRL, Generative Classifier) in three different scenarios.
Is continual-learning open source?
continual-learning is an open-source project. It is released under the MIT license.
Is continual-learning free?
Yes. continual-learning is free and open source — you can use, modify and self-host it.
What license does continual-learning use?
continual-learning is available under the MIT license.
What language is continual-learning written in?
continual-learning is written mainly in Jupyter Notebook.
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