We present Prompt-aligned Gradient, dubbed ProGrad, to prevent prompt tuning from forgetting the the general knowledge learned from VLMs. In particular, ProGrad only updates the prompt whose gradient is aligned (or non-conflicting) to the “general direction”, which is represented as the gradient of the KL loss of the pre-defined prompt prediction. Extensive experiments demonstrate the stronger few-shot generalization ability of ProGrad over state-of-the-art prompt tuning methods.
The codes are organized into two folders:
- Dassl.ProGrad.pytorch is the modified toolbox of Dassl.pytorch.
- ProGrad.public. To get the results in our paper, follow the README.md under ProGrad.public/ to set the environment.
If you find our paper or this project helps your research, please kindly consider citing our paper in your publication.
@misc{https://doi.org/10.48550/arxiv.2205.14865,
doi = {10.48550/ARXIV.2205.14865},
url = {https://arxiv.org/abs/2205.14865},
author = {Zhu, Beier and Niu, Yulei and Han, Yucheng and Wu, Yue and Zhang, Hanwang},
title = {Prompt-aligned Gradient for Prompt Tuning},
publisher = {arXiv},
year = {2022},
}