This is the official PyTorch implementation for the SpaceNet: Make Free Space For Continual Learning paper in Elsevier Neurocomputing Journal.

In this work, we present a new brain-inspired method to learn a sequence of tasks sequentially. SpaceNet learns semi-distributed sparse representation to mitigate forgetting previously learned knowledge. We train each task using sparse connections from scratch. Our proposed adaptive sparse training algorithm dynamically redistributes and compacts the sparse connections in the important neurons to the current task leaving free space for future tasks.

• Python 3.6
• Pytorch 1.2
• torchvision 0.4

You can use main.py to run SpaceNet on the Split MNIST benchmark.

python main.py

If you use this code, please cite our paper:

@article{SOKAR20211,
title = {SpaceNet: Make Free Space for Continual Learning},
journal = {Neurocomputing},
volume = {439},
pages = {1-11},
year = {2021},
issn = {0925-2312},
doi = {https://doi.org/10.1016/j.neucom.2021.01.078},
url = {https://www.sciencedirect.com/science/article/pii/S0925231221001545},
author = {Ghada Sokar and Decebal Constantin Mocanu and Mykola Pechenizkiy}
}