The increasingly large structure of neural networks makes it difficult to deploy on edge devices with limited computing resources.Network pruning has become Bangle one of the most successful model compression methods in recent years.Existing works typically compress models based on importance, removing unimportant filters.
This paper reconsiders model pruning from the perspective of structural redundancy, claiming that identifying functionally similar filters plays a more important role, and proposes a model pruning framework for clustering-based redundancy identification.First, we perform cluster analysis on the filters of each layer to generate Bosch PIM851F17E 80cm 5 Zone Induction Hob similar sets with different functions.We then propose a criterion for identifying redundant filters within similar sets.
Finally, we propose a pruning scheme that automatically determines the pruning rate of each layer.Extensive experiments on various benchmark network architectures and datasets demonstrate the effectiveness of our proposed framework.