ANCL-master

This is official code implementation of the <Achieving a Better Stability-Plasticity Trade-off via Auxiliary Networks in Continual Learning> in CVPR 2023

How To Use

We implement our code based on FACIL available online https://github.com/mmasana/FACIL

Below instructions are from FACIL directory and no additional library is required to run our code. Please refer to above link to find more information to run code with different setting.

Optionally, create an environment to run the code (click to expand).

Using a requirements file

The library requirements of the code are detailed in requirements.txt. You can install them using pip with:

python3 -m pip install -r requirements.txt

Using a conda environment

Development environment based on Conda distribution. All dependencies are in environment.yml file.

Create env

To create a new environment check out the repository and type:

conda env create --file environment.yml --name FACIL

Notice: set the appropriate version of your CUDA driver for cudatoolkit in environment.yml.

Environment activation/deactivation

conda activate FACIL
conda deactivate

Citation

Please cite our paper if it is helpful to your work:

@InProceedings{Kim_2023_CVPR,
    author    = {Kim, Sanghwan and Noci, Lorenzo and Orvieto, Antonio and Hofmann, Thomas},
    title     = {Achieving a Better Stability-Plasticity Trade-Off via Auxiliary Networks in Continual Learning},
    booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
    month     = {June},
    year      = {2023},
    pages     = {11930-11939}
}