From Deep to Shallow: Unconstrained and Efficient Layer Merging Strategy
为应对深度神经网络在资源受限环境中计算需求高的问题，研究者提出了一种无需分析解且无需增加卷积核大小的层合并策略。该方法解决了现有方法无法直接应用于带填充卷积以及通常导致合并后卷积核增大从而限制加速比的问题。研究者在多种架构和数据集上验证了该方案的有效性，并在真实嵌入式平台上测量了推理加速收益。相关代码已公开发布于 GitHub 平台。
SummaryAI generated
On September 7, 2026, arXiv cs.LG published the paper “From Deep to Shallow: Unconstrained and Efficient Layer Merging Strategy”, which proposes a new layer merging strategy. This strategy aims to achieve unconstrained and efficient layer merging of models, in order to optimize model compression and inference efficiency.
Related eventsRELATED EVENTS
Key entitiesKEY ENTITIES
Deep Neural NetworksarXiv:2609.04881v1
Coverage · reports per dayLANGUAGE SPLIT
Entity relations
SignalsSIGNALS
Keyword heat