Code for paper "Boosting Continual Learning of Vision-Language Models via Mixture-of-Experts Adapters" CVPR2024
Continual learning can empower vision-language models to continuously acquire new knowledge, without the need for access to the entire historical dataset. However, mitigating the performance degradation in large-scale models is non-trivial due to (i) parameter shifts throughout lifelong learning and (ii) significant computational burdens associated with full-model tuning. In this work, we present a parameter-efficient continual learning framework to alleviate long-term forgetting in incremental learning with vision-language models. Our approach involves the dynamic expansion of a pre-trained CLIP model, through the integration of Mixture-of-Experts (MoE) adapters in response to new tasks. To preserve the zero-shot recognition capability of vision-language models, we further introduce a Distribution Discriminative Auto-Selector (DDAS) that automatically routes in-distribution and out-of-distribution inputs to the MoE Adapter and the original CLIP, respectively. Through extensive experiments across various settings, our proposed method consistently outperforms previous state-of-the-art approaches while concurrently reducing parameter training burdens by 60%.
conda create -n MoE_Adapters4CL python=3.9
conda activate MoE_Adapters4CL
conda install pytorch==2.1.0 torchvision==0.16.0 torchaudio==2.1.0 pytorch-cuda=11.8 -c pytorch -c nvidia
cd cil
pip install -r requirements.txt
Target Datasets: Aircraft, Caltech101,CIFAR10, CIFAR100, DTD, EuroSAT, Flowers, Food, MNIST, OxfordPet,StanfordCars, SUN397, TinyImagenet.
If you have problems with Caltech101, you can refer to issue#6.
More details can refer to datasets.md of ZSCL. Big thanks to them for their awesome work!
Model | Link | |
---|---|---|
full_shot_order1 | full_shot_order1_1000iters.pth | Baidu Disk / Google Drive |
few_shot_order1 | few_shot_order1_1000iters.pth | Baidu Disk / Google Drive |
Example:
cd MoE-Adapters4CL/mtil
bash srcipts/test/Full_Shot_order1.sh
Example:
cd MoE-Adapters4CL/mtil
bash srcipts/train/train_full_shot_router11_experts22_1000iters.sh
Example:
cd cil
bash run_cifar100-2-2.sh
@inproceedings{yu2024boosting,
title={Boosting continual learning of vision-language models via mixture-of-experts adapters},
author={Yu, Jiazuo and Zhuge, Yunzhi and Zhang, Lu and Hu, Ping and Wang, Dong and Lu, Huchuan and He, You},
booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
pages={23219--23230},
year={2024}
}
Our repo is built on wise-ft, Continual-CLIP and ZSCL. We thank the authors for sharing their codes.