Pear：视觉参数高效微调中的适配器剪枝与共享
Pear: Pruning and Sharing Adapters in Visual Parameter-Efficient Fine-Tuning
AI总结：
Pear提出对视觉微调中的冗余适配器进行剪枝，并将重要未剪枝适配器共享到剪枝位置，同时引入知识检查点保留剪枝信息，从而在降低存储开销的同时提升视觉适配性能。
摘要：
在微调预训练基础模型时，适配器已被广泛探索以降低计算和存储成本。然而，适配器本身可能存在冗余，从而导致不必要的存储开销和性能下降。本文提出了Prune and Share（Pear），一种用于预训练视觉基础模型高效微调的新型适配器剪枝框架。具体而言，我们剪除部分适配器，并将更重要的未剪枝适配器共享到适配器被剪除的位置，从而在剪枝后仍能在这些位置持续适配。此外，还引入了知识检查点策略，该策略保留被剪枝适配器的信息并进一步提升性能。在视觉适配基准上的实验结果验证了所提出的Pear相较于其他竞争方法的有效性和高效性。代码见https://github.com/yibozhong/pear。
英文摘要：
Adapters have been widely explored to alleviate computational and storage costs when fine-tuning pretrained foundation models. However, the adapter itself can exhibit redundancy, leading to unnecessary storage overhead and inferior performance. In this paper, we propose Prune and Share (Pear), a novel adapter-pruning framework for efficient fine-tuning of pretrained visual foundation models. Specifically, we prune certain adapters and share the more important unpruned ones with positions where adapters are pruned, allowing continual adaptation at these positions after pruning. Additionally, a knowledge checkpoint strategy is introduced, which preserves the information of the pruned adapters and further boosts performance. Experimental results on visual adaptation benchmark validate the effectiveness and efficiency of the proposed Pear comparing to other competitive methods. Code is in https://github.com/yibozhong/pear.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
