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cs.CV2024
Pear: Pruning and Sharing Adapters in Visual Parameter-Efficient Fine-Tuning
Yibo Zhong, Yao Zhou
Adapters have been widely explored to alleviate computational and storage costs when fine-tuning pretrained foundation models. However, the adapter itself can exhibit redundancy, l…
cs.CV2024
Low-Rank Interconnected Adaptation across Layers
Yibo Zhong, Jinman Zhao, Yao Zhou
Low-rank adaptation (LoRA) is a widely used parameter-efficient fine-tuning (PEFT) method that learns weight updates for pretrained weights through low-rank adapters…
cs.CV2024
Rethinking Low-Rank Adaptation in Vision: Exploring Head-Level Responsiveness across Diverse Tasks
Yibo Zhong, Yao Zhou
Low-rank adaptation (LoRA) has shifted the paradigm of adapting pre-trained Vision Transformers (ViT), achieving great efficiency by updating only a subset of tailored parameters t…