Continual Learning with Vistion Transformers

This repo hosts the official implementation of our CVPR 2022 workshop paper Towards Exemplar-Free Continual Learning in Vision Transformers: an Account of Attention, Functional and Weight Regularization.

Running the code

Given below are two examples for the asymmetric attentional and functional variants pooling along the height dimension on ImageNet-100.

1. Attentional variant:

>>> python3 -u src/main_incremental.py --datasets imagenet_32_reduced --network Early_conv_vit --approach olwf_asym --nepochs $NEPOCHS --log disk --batch-size 1024 --gpu $GPU --exp-name dummy_attentional_exp --lr 0.01 --seed ${seed} --lamb 1.0 --num-tasks $NUM_TASKS --nc-first-task $NC_FIRST_TASK --lr-patience 20 --plast_mu 1.0 --pool-along 'height'   l

2. Functional variant:

>>> python3 -u src/main_incremental.py --datasets imagenet_32_reduced --network Early_conv_vit --approach olwf_asympost --nepochs $NEPOCHS --log disk --batch-size 1024 --gpu $GPU --exp-name dummy_functional_exp --lr 0.01 --seed ${seed} --lamb 1.0 --num-tasks $NUM_TASKS --nc-first-task $NC_FIRST_TASK --lr-patience 20 --plast_mu 1.0 --pool-along 'height'   

The corresponding runs for symmetric variants would then be:

1. Attentional variant:

>>> python3 -u src/main_incremental.py --datasets imagenet_32_reduced --network Early_conv_vit --approach olwf_asym --nepochs $NEPOCHS --log disk --batch-size 1024 --gpu $GPU --exp-name dummy_attentional_exp --lr 0.01 --seed ${seed} --lamb 1.0 --num-tasks $NUM_TASKS --nc-first-task $NC_FIRST_TASK --lr-patience 20 --plast_mu 1.0 --pool-along 'height' --sym 

2. Functional variant:

>>> python3 -u src/main_incremental.py --datasets imagenet_32_reduced --network Early_conv_vit --approach olwf_asympost --nepochs $NEPOCHS --log disk --batch-size 1024 --gpu $GPU --exp-name dummy_functional_exp --lr 0.01 --seed ${seed} --lamb 1.0 --num-tasks $NUM_TASKS --nc-first-task $NC_FIRST_TASK --lr-patience 20 --plast_mu 1.0 --pool-along 'height' --sym 

Current available approaches with Vision Transformers include:

EWC • Finetuning

LwF • PathInt

The detailed scripts for our experiments can be found in `scripts/`.

Cite

If you found our implementation to be useful, feel free to use the citation:

@inproceedings{pelosin2022towards,
  title={Towards exemplar-free continual learning in vision transformers: an account of attention, functional and weight regularization},
  author={Pelosin, Francesco and Jha, Saurav and Torsello, Andrea and Raducanu, Bogdan and van de Weijer, Joost},
  booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
  pages={3820--3829},
  year={2022}
}

Acknowledgement

This repo is based on FACIL.