GMvandeVen/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.
This project helps machine learning researchers and practitioners evaluate and compare different continual learning strategies for deep neural networks. It takes various classification datasets, split into sequential 'contexts' or 'tasks', and applies different continual learning methods. The output includes performance metrics and visualizations that show how well a model learns new information without forgetting previously learned knowledge.
1,832 stars.
Use this if you are a machine learning researcher or engineer interested in experimenting with and comparing various continual learning algorithms for classification tasks.
Not ideal if you need a pre-built solution for a specific real-world application or are not familiar with deep learning experimental setups.
Stars
1,832
Forks
345
Language
Jupyter Notebook
License
MIT
Category
Last pushed
Nov 05, 2025
Commits (30d)
0
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