huypl53/MoE-Adapters4CL

Code for paper "Boosting Continual Learning of Vision-Language Models via Mixture-of-Experts Adapters" CVPR2024

MoE-Adapters4CL

Code for paper "Boosting Continual Learning of Vision-Language Models via Mixture-of-Experts Adapters" CVPR2024

Table of Contents

• Abstract
• Approach
• Install
• Data preparation
• Getting Started
  • Model ckpt
  • MTCL
    • Test
    • Train
  • CIL
    • Train
• Citation
• Acknowledgement

Abstract

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%.

Approach

---

Install

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

Data preparation

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 ckpt

	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

MTCL

Test stage

Example:

1. Move the checkpoints to MoE-Adapters4CL/ckpt
2. cd MoE-Adapters4CL/mtil
3. Run the script bash srcipts/test/Full_Shot_order1.sh

Train stage

Example:

1. Move the checkpoints to MoE-Adapters4CL/ckpt
2. cd MoE-Adapters4CL/mtil
3. Run the script bash srcipts/train/train_full_shot_router11_experts22_1000iters.sh

Class Incremental Learning

Train stage

Example:

1. cd cil
2. bash run_cifar100-2-2.sh

Citation

@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 (CVPR)},
  year={2024}
}

Acknowledgement

Our repo is built on wise-ft, Continual-CLIP and ZSCL. We thank the authors for sharing their codes.