Papers › Gradient-based Parameter Selection for Efficient Fine-Tuning

Gradient-based Parameter Selection for Efficient Fine-Tuning

15 Dec 2023CVPR 2024 1arXiv:2312.10136archive 2025-07-28

Zhi Zhang, Qizhe Zhang, Zijun Gao, Renrui Zhang, Ekaterina Shutova, Shiji Zhou, Shanghang Zhang

With the growing size of pre-trained models, full fine-tuning and storing all the parameters for various downstream tasks is costly and infeasible. In this paper, we propose a new parameter-efficient fine-tuning method, Gradient-based Parameter Selection (GPS), demonstrating that only tuning a few selected parameters from the pre-trained model while keeping the remainder of the model frozen can generate similar or better performance compared with the full model fine-tuning method. Different from the existing popular and state-of-the-art parameter-efficient fine-tuning approaches, our method does not introduce any additional parameters and computational costs during both the training and inference stages. Another advantage is the model-agnostic and non-destructive property, which eliminates the need for any other design specific to a particular model. Compared with the full fine-tuning, GPS achieves 3.33% (91.78% vs. 88.45%, FGVC) and 9.61% (73.1% vs. 65.57%, VTAB) improvement of the accuracy with tuning only 0.36% parameters of the pre-trained model on average over 24 image classification tasks; it also demonstrates a significant improvement of 17% and 16.8% in mDice and mIoU, respectively, on medical image segmentation task. Moreover, GPS achieves state-of-the-art performance compared with existing PEFT methods.

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FightingFighting/GPS officialmentioned in papermentioned on GitHubpytorchMIT report

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1ran · honoured contract
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MyNorm FightingFighting/GPS/models/as_mlp.py official repository ran · our draft was wrong MIT (permissive) · c3f4c265067c1717 · report
get_init_weights_vit FightingFighting/GPS/models/vision_transformer.py official repository ran MIT (permissive) · def23f7d7d042968 · report
init_ssf_scale_shift FightingFighting/GPS/models/vision_transformer.py official repository ran · our draft was wrong fingerprinted MIT (permissive) · 896b5eec245f1bab · report
init_ssf_scale_shift FightingFighting/GPS/models/as_mlp.py official repository ran · our draft was wrong fingerprinted MIT (permissive) · 9873773b10315cc0 · report
param_groups_weight_decay FightingFighting/GPS/optim_factory.py official repository ran · our draft was wrong MIT (permissive) · 88f4962784e09b88 · report
ssf_ada FightingFighting/GPS/models/as_mlp.py official repository ran · honoured contract fingerprinted MIT (permissive) · 61e8364b7e9bcb70 · report
window_partition FightingFighting/GPS/models/swin_transformer.py official repository ran · fixture could not drive it fingerprinted MIT (permissive) · 993ae96666b00cb5 · report
window_reverse FightingFighting/GPS/models/swin_transformer.py official repository ran · our draft was wrong MIT (permissive) · 609922bd93c75117 · report
checkpoint_filter_fn FightingFighting/GPS/models/convnext.py official repository unverified MIT (permissive) · 7619bf7f57c5d2b6 · report
group_parameters FightingFighting/GPS/optim_factory.py official repository unverified MIT (permissive) · efeb37d90ba28266 · report
group_with_matcher FightingFighting/GPS/optim_factory.py official repository unverified MIT (permissive) · 0332519996466b5a · report
validate FightingFighting/GPS/train_gps.py official repository unverified MIT (permissive) · e2a98c6a53ded11f · report

Tasks

Image ClassificationImage SegmentationMedical Image SegmentationSemantic Segmentationimage-classificationparameter-efficient fine-tuning

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Methods

GPS

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