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.

本研究针对适配器的冗余性问题，提出了Prune and Share（Pear）框架，通过修剪不必要的适配器并共享重要的适配器，提升视觉预训练模型的微调效率。此外，采用知识检查点策略保留修剪适配器的信息，进一步提高性能。实验结果表明该方法在视觉适应基准测试中优于其他竞争方法。

梨：在视觉参数高效微调中修剪和共享适配器