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Ex-model CL

Ex-model continual learning is a setting where a stream of experts (i.e. model's parameters) is available and a continual learning model learns from them without access to the original data.

ExML scenario

This is based on our recent work on continual learning from pretrained models. This repository provides a snapshot of the codebase at the time of publication. If you want to test your own strategies, the benchmarks and pretrained models are available directly in Avalanche, and you should use them instead of this repository.

We plan to add also the Ex-Model Distillation inside Avalanche in the future.

NOTE: This repository is a heavy refactoring of the original codebase which was used to run the experiment. The refactoring was necessary to make it easier to understand and reuse by other researchers. However, due to the high variance of the experiements, there may be slight differences in the results compared to the paper.

The module exmodel follows Avalanche's structure:

  • benchmarks: ExModelScenario adds an attribute trained_models to the benchmarks. The original train_stream and test_stream are available for evaluation purposes (they are assumed private by the scenario).
  • models: custom nn.Modules and baseline architectures used in the experiments.
  • evaluation: loggers and metrics.
  • training: training algorithms. The ex-model distillation strategy is here.

The folder experiments contains the code to run the experiments. The main is in launcher.py, while the training function is in train_ex_model.py.

Install Dependencies

conda env create -f environment.yml

avalanche must be installed separately. This repository used Avalanche pre-release, commit a299bd4.

Run Experiments

to launch an experiment run:

python experiments/launcher.py --config CONFIGS/debug.yaml

The directory CONFIGS contains the configuration already setup for you. To run the experiments you may need to change the logs and data folders in the CONFIGS yaml files.

Known issues

The table printed by the rich-based logger sometimes misalign metric values (when there are missing values). The textual logger and json files are all correct.

Citation

If you find this useful consider citing:

Carta, A., Cossu, A., Lomonaco, V., & Bacciu, D. (2021). Ex-Model: Continual Learning from a Stream of Trained Models. arXiv preprint arXiv:2112.06511.

bibtex:

@article{carta2021ex,
  title={Ex-Model: Continual Learning from a Stream of Trained Models},
  author={Carta, Antonio and Cossu, Andrea and Lomonaco, Vincenzo and Bacciu, Davide},
  journal={arXiv preprint arXiv:2112.06511},
  year={2021}
}