Convolutional Prompting meets Language Models for Continual Learning [Paper] [Website]

This repository contains the implementation details of our Convolutional Prompting meets Language Models for Continual Learning (ConvPrompt) approach for continual learning with transformer backbone.

Anurag Roy, Riddhiman Moulick, Vinay K. Verma, Saptarshi Ghosh, Abir Das, "Convolutional Prompting meets Language Models for Continual Learning"

If you use the codes and models from this repo, please cite our work. Thanks!

@InProceedings{roy_2024_CVPR,
    author    = {Roy, Anurag and Moulick, Riddhiman and Verma, Vinay and Ghosh, Saptarshi and Das, Abir},
    title     = {Convolutional Prompting meets Language Models for Continual Learning},
    booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
    month     = {June},
    year      = {2024}
}

Requirements

The code is written for python 3.8.18, but should work for other version with some modifications.

pip install -r requirements.txt

Data preparation

If you already have CIFAR-100/ImageNet-R/CUB-200, pass your dataset path to the --data-path argument during execution (If they aren't ready they will automatically get downloaded to the data-path mentioned when the download argument is kept True in datasets.py).

Descriptor generation

The descriptors for the datasets that have been used have been stored in the format ./descriptors/descriptors_{dataset_name}. For the generation of new descriptors, one can refer to the code from Visual Classification via Description from Large Language Models.
(Note: The generation of any such task-similarity metric such as descriptors is a one-time process)

Training

Use the following command for training:

export TOKENIZERS_PARALLELISM=false
python -m main <cifar100_convprompt or imr_convprompt or cub_convprompt> \
        --num_tasks 10 \
        --data-path /local_datasets/ \
        --output_dir ./output 

Acknowledgement

This repo is heavily based on the DualPrompt Implementation.