Awesome
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.
<details> <summary>Optionally, create an environment to run the code (click to expand).</summary>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
</details>
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}
}