ICCPNet: inter-layer coupling channel pruning network based on elastic scaling scoring for complex traffic scene
摘要
The massive scale of deep learning models limits their application on edge devices. Therefore, reducing the number and complexity of model parameters has become a research hotspot. The existing model pruning methods do not consider the interlayer coupling relationship between channels, resulting in the loss of some correlation information and low detection accuracy. Therefore, we propose an interlayer coupled pruning method based on elastic scaling score factors. Firstly, considering that the current layer filter may have an impact on subsequent channels, an intra layer coupled channel pruning method is proposed by adding inter layer coupling relationships to determine whether the channel is redundant based on channel pruning. Secondly, in the process of sparsification, to more accurately evaluate the importance of channels, L1-norm and L2-norm were combined for sparse regularization, and an elastic scaling scoring factor