Keskeiset käsitteet
The author proposes an enhanced sparsification paradigm for structured pruning to address capacity damage issues before pruning. This approach aims to maintain network performance while reducing FLOPs effectively.
Tiivistelmä
The content discusses the challenges of suppressed sparsification paradigms in structured pruning and introduces a novel approach based on relative sparsity effects in Stimulative Training. The proposed method, STP, demonstrates superior performance across various benchmarks and networks, especially under aggressive pruning scenarios. Extensive experiments validate the effectiveness of STP in achieving high performance with significantly reduced FLOPs.
Tilastot
"Extensive experiments on various benchmarks indicate the effectiveness of STP."
"Remaining 95.11% Top-1 accuracy while reducing 85% FLOPs for ResNet-50 on ImageNet."
"Preserving 95.11% performance while reducing 85% FLOPs on ImageNet."
"Maintains high accuracy even under extremely low FLOPs intervals."
"STP consistently achieves state-of-the-art performance compared to other methods."