Online Fast Adaptation and Knowledge Accumulation:

a New Approach to Continual Learning

official code for the paper: https://arxiv.org/abs/2003.05856

(key) Requirements

• Python 3.6
• Pytorch 1.2 or higher

pip install -r requirements.txt

Structure

├── Data
    ├── omniglot.py           # fetches the dataset      
    ├── tiered_imagenet.py    # fetches the dataset
├── MAML           
    ├── metalearner
        ├── maml.py           # defines the models, in particular their CL strategy
    ├── model.py              # defines the backbone neural networks
    ├── utils.py              # some utils    
├── Utils
    ├── bgd_lib
        ├── ...         # files for BGD      
├── main.py             # main file
├── dataloaders.py      # defines the experiment setting, constructs the dataloaders    
├── args.py             # arguments
├── template.py         # main file template (if you dont want to use pytorch)

Running Experiments

example (in verbose mode):

python main.py -v

some notable args:

python main.py --prob_statio 0.98 --num_epochs 0 --cl_strategy always_retrain --meta_lr 0.1 --learn_step_size --per_parameter_step_size -v 

to try different baselines in ['online_sgd', 'fine_tuning', 'MetaCOG', 'MetaBGD', 'MAML','ANIL', 'BGD']

python main.py --model_name <baseline_name>

Logging

Logging is done with Weights & Biases and can be turned on like this:
python main.py --wandb <workspace_name>

TODO

• code to reproduce experiments

Acknowledgements

MAML code comes from https://github.com/tristandeleu/pytorch-maml

Contact

massimo.p.caccia at gmail.com