Easy Pruning via Coresets and Structural Re-Parameterization

Wenfeng Yin, Gang Dong, Dianzheng An, Yaqian Zhao, Binqiang Wang

Abstract: Inference time pruning is characteristic in high construction efficiency, since it dramatically reduces the dependency on finetuning to recover precision. It is adequate to reconstruct compressed convolution kernels by optimizing the loss of feature map reconstruction. However, the accuracy decline of compressed network increases as the loss of feature map reconstruction accumulates layer by layer. To enhance layerwise convolution kernel reconstruction, this paper proposes a hybrid method via combining coresets theory and structural re-parameterization, enabling shallow transfer learning (STL) during inference time pruning. Firstly, our method achieves STL by implementing structural re-parameterization in the process of convolution kernel reconstruction, to adapt to effects of one layer's reconstruction loss on the next layers' inputs. Secondly, a channel-wise scaling process is designed on the basis of coresets theory, to enhance approximation in the mapping from drifted inputs to original feature maps. Selectively, a maximum mean discrepancy based decision-making process is built for switching in two patterns of our method. Tests are executed on image classification and arrhythmia detection. As observed on ImageNet datasets, coresets theory based scaling is more effective at filter level for DenseNet and MobileNet-v2 and resultful at unit kernel level for ResNet and SqueezeNet.