This is a PyTorch implementation of the continual learning experiments described in the following paper:

• Gido M. van de Ven, Andreas S. Tolias (2018) Generative replay with feedback connections as a general strategy for continual learning, arXiv preprint

The current version of the code has been tested with:

• pytorch 0.4.1
• torchvision 0.2.1

Individual experiments can be run with main.py. Main options are:

• --experiment: which task protocol should be used? (splitMNIST|permMNIST)
• --scenario: according to which continual learning scenario should this protocol be performed? (task|domain|class)

To run specific methods, use the following:

• Elastic weight consolidation (EWC): ./main.py --ewc
• Online EWC: ./main.py --ewc --online
• Synaptic intelligenc (SI): ./main.py --si
• Context-dependent-Gating (XdG): ./main.py --XdG=0.8
• Deep Generative Replay (DGR): ./main.py --replay=generative
• DGR + distillation: ./main.py --replay=generative --distill
• Replay-trough-Feedback (RtF): ./main.py --replay=generative --distill --feedback

For information on further options: ./main.py -h.

A comparison of all methods used in our paper can be run with compare_MNIST.py.

This code enables tracking of progress during training with on-the-fly plots. This feature requires visdom, which can be installed as follows:

pip install visdom

Before running the experiments, the visdom server should be started from the command line:

python -m visdom.server

The visdom server is now alive and can be accessed at http://localhost:8097 in your browser (the plots will appear here). The flag --visdom should then be added when calling ./main.py to run the experiments with on-the-fly plots.

For more information on visdom see https://github.com/facebookresearch/visdom.

Please consider citing our paper if you use this code in your research:

@article{vandeven2018generative,
  title={Generative replay with feedback connections as a general strategy for continual learning},
  author={van de Ven, Gido M and Tolias, Andreas S},
  journal={arXiv preprint arXiv:1809.10635},
  year={2018}
}

The research project from which this code originated has been supported by an IBRO-ISN Research Fellowship, by the Lifelong Learning Machines (L2M) program of the Defence Advanced Research Projects Agency (DARPA) via contract number HR0011-18-2-0025 and by the Intelligence Advanced Research Projects Activity (IARPA) via Department of Interior/Interior Business Center (DoI/IBC) contract number D16PC00003. Disclaimer: views and conclusions contained herein are those of the authors and should not be interpreted as necessarily representing the official policies or endorsements, either expressed or implied, of DARPA, IARPA, DoI/IBC, or the U.S. Government.