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Status: Unchecked ×Keyword: frozen pre-trained models ×Clear all

3 claims from 1 paper

1. Computer Science › Domain Adaptation and Few-Shot Learning

   Gradient-based Parameter Selection for Efficient Fine-Tuning

   Zhang, Zhang, Gao et al. · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · 2024

   The paper proposes Gradient-based Parameter Selection (GPS), which tunes only a few selected parameters of a pre-trained model and is reported to match or beat full fine-tuning and other efficient methods.

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   1. UncheckedGPS, tuning only 0.36% of a pre-trained model's parameters, is reported to beat full fine-tuning on 24 image classification tasks and on medical image segmentation.“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…”
   2. Unchecked“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.”
   3. Unchecked“Moreover, GPS achieves state-of-the-art performance compared with existing PEFT methods.”