Channel-Pruning Convolutional Neural Network with Learnable Kernel Element Position Convolution Utilizing the Symmetric Whittaker–Shannon Interpolation Function

Large convolution kernels offer better performance advantages. They can cover a wider area and capture a broader range of spatial information in a single convolution operation. This is of great importance when dealing with tasks that have significant spatial variations. However, increasing the kernel size brings substantial memory and computational costs to deep convolutional neural networks. The computational complexity becomes unimaginable. Therefore, we proposed a learnable kernel element position convolution using the symmetric Whittaker–Shannon interpolation function (WSIPC). We also performed channel-level pruning (CP) on this large convolutional neural network to achieve network compression. Specifically, WSIPC permits any number of kernel elements. The positions of non-zero elements are learned in a gradient-based manner. We made use of the normal distribution of effective receptive fields to reduce computation, parameter complexity, and improve classification performance. This method achieved the best performance on the large-kernel ConvNeXt. CP used the scaling factor in the Layer Normalization (LN) of the ConvNeXt network as a proxy for channel selection. During training, it automatically identified and pruned unimportant channels in wide and large network models. As a result, it produced a streamlined model with comparable accuracy. This model was more compact in terms of model size, runtime memory, and computational operations.