This repository contains code to reproduce the key findings of our path integral approach to prevent catastrophic forgetting in continual learning.

Zenke, F.¹, Poole, B.¹, and Ganguli, S. (2017). Continual Learning Through Synaptic Intelligence. In Proceedings of the 34th International Conference on Machine Learning, D. Precup, and Y.W. Teh, eds. (International Convention Centre, Sydney, Australia: PMLR), pp. 3987–3995.

http://proceedings.mlr.press/v70/zenke17a.html

¹) Equal contribution

@InProceedings{pmlr-v70-zenke17a,
title = 	 {Continual Learning Through Synaptic Intelligence},
author = 	 {Friedemann Zenke and Ben Poole and Surya Ganguli},
booktitle = 	 {Proceedings of the 34th International Conference on Machine Learning},
pages = 	 {3987--3995},
year = 	 {2017},
editor = 	 {Doina Precup and Yee Whye Teh},
volume = 	 {70},
series = 	 {Proceedings of Machine Learning Research},
address = 	 {International Convention Centre, Sydney, Australia},
month = 	 {06--11 Aug},
publisher = 	 {PMLR},
pdf = 	 {http://proceedings.mlr.press/v70/zenke17a/zenke17a.pdf},
url = 	 {http://proceedings.mlr.press/v70/zenke17a.html},
}

We have tested this maintenance release (v1.1) with the following configuration:

• Python 3.5.2
• Jupyter 4.4.0
• Tensorflow 1.10
• Keras 2.2.2

Kudos to Mitra (https://github.com/MitraDarja) for making our code conform with Keras 2.2.2!

For the original release (v1.0) we used the following configuration of the libraries which were available at the time:

• Python 3.5.2
• Jupyter 4.3.0
• Tensorflow 1.2.1
• Keras 2.0.5

To revert to such a environment we suggest using virtualenv (https://virtualenv.pypa.io):

virtualenv -p python3 env
source env/bin/activate
pip3 install -vI keras==2.0.5
pip3 install jupyter matplotlib numpy tensorflow-gpu tqdm seaborn