.png){kind=icon}
.png){kind=icon}
Return
CorrNet: pearson correlation based pruning for efficient convolutional neural networks
.png){kind=icon}
PRE
AI
DOI：10.1007/s13042-022-01624-5.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
Convolutional neural networks (CNNs) are quickly evolving, which usually results in a surge of computational cost and model size. In this article, we present a correlation-based filter pruning (CFP) approach to train more reliable CNN models. Unlike several available filter pruning methodologies, our presented approach eliminates useless filters according to the volume of information available in their related feature maps. We apply correlation to compute the duplication of information carried in the feature maps and created a feature selection scheme to obtain pruning approaches. Pruning and fine-tuning are cycled many times, producing slim and denser networks with similar accuracy to the original unpruned model. We practically calculate the success of our technique with various state-of-art CNN models on many standard datasets. Specifically, for ResNet-50 on ImageNet, our approach eliminates 44.6% filter weights and saves 51.6% Float-Point-Operations (FLOPs) with 0.5% accuracy gain and obtained state-of-art performance.
Keywords:
Correlation
Feature maps
FLOPs
ResNet
ImageNet
Journal
IF:
2.7
Papers:
3.1K
Citations:
5.6K