PACE: Marrying generalization in PArameter-efficient fine-tuning with Consistency rEgularization (NeurIPS 2024 Spotlight)

Yao Ni , Shan Zhang , Piotr Koniusz

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Below are the general knowledges discovered from our work:

💡 Lower gradient norms improve model generalization.

💡 Consistency regularization across different perturbations reduces gradient norms, improving generalization.

💡 Consistency regularization on adapter features aligns fine-tuned models with pre-trained ones, preserving knowledge.

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Code for PACE on VTAB-1K and Few-Shot Learning is Released.

Citation

If you find the theories or code help your work, please kindly cite our paper:

@inproceedings{
ni2024pace,
title={{PACE}: marrying the generalization of {PA}rameter-efficient fine-tuning with Consistency rEgularization},
author={Yao Ni and Shan Zhang and Piotr Koniusz},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=cOuLbPhOT1}
}