LORS addresses the issue of parameter explosion in deep learning models with stacked structures. By sharing parameters across modules and retaining unique ones, LORS significantly reduces total parameters without compromising performance. Experimental results on object detection tasks demonstrate the effectiveness of LORS in achieving up to 70% reduction in decoder parameters while maintaining or even improving model performance.
To Another Language
from source content
arxiv.org
Key Insights Distilled From
by Jialin Li,Qi... at arxiv.org 03-08-2024
https://arxiv.org/pdf/2403.04303.pdfDeeper Inquiries