Awesome

Continual Learning with OGD and OGD+

This is the official implementation of the article Generalisation Guarantees for Continual Learning with Orthogonal Gradient Descent in PyTorch.

Requirements

• I ran most of the experiments with PyTorch = 1.5.1
• The precie environment configuration is presented in requirements.txt :)

Reproducibility

Running the experiments

In order to replicate the results of the paper, please refer to the scripts provided in the scripts directory.

• The production scripts have a prefix prod.
• The ablation studies scripts have a prefix ablation.
• The grid search scripts have a prefix gs.

Grid search results

The grid search results are provided under the results directory :

• gs_dynamics_* contain the training dynamics results comprising the validation accuracy at the end of each task
• gs_metrics_* are summary files which contain the Continual Learning metrics AAC, FWT, BWT and AFM for each run

Questions/ Bugs

• For questions or bugs, please feel free to contact me or to raise an issue on Github :)

Licence

Continual-Learning-Benchmark

A substantial part of this source code was initially forked from the repository GT-RIPL/Continual-Learning-Benchmark . The corresponding Licence is also provided in the root directory.
The work related to the original source code is the following :

@inproceedings{Hsu18_EvalCL,
 title={Re-evaluating Continual Learning Scenarios: A Categorization and Case for Strong Baselines},
 author={Yen-Chang Hsu and Yen-Cheng Liu and Anita Ramasamy and Zsolt Kira},
 booktitle={NeurIPS Continual learning Workshop },
 year={2018},
 url={https://arxiv.org/abs/1810.12488}
}

It was released under The MIT License found in the LICENSE file in the root directory of this source tree.

stable-continual-learning

The Stable SGD code in the external folder was forked from this repository.

Since I brought some changes to it, for reproducibility experiments of the original paper, I recommend to fork the original codebase. This fork may also not be up to date.

The modifications I brought were for logging, consistency with the other benchmarks and in order to run the experiments on other datasets.

Please let me know if you have any issues :)