Dynamic convolution achieves a substantial performance boost for efficient CNNs at a cost of increased convolutional weights. Contrastively, mask-based unstructured pruning obtains a lightweight network by removing redundancy in the heavy network at risk of performance drop. In this paper, we propose a new framework to coherently integrate these two paths so that they can complement each other compensate for the disadvantages. We first design a binary mask derived from a learnable threshold to prune static kernels, significantly reducing the parameters and computational cost but achieving higher performance in Imagenet-1K(0.6\% increase in top-1 accuracy with 0.67G fewer FLOPs). Based on this learnable mask, we further propose a novel dynamic sparse network incorporating the dynamic routine mechanism, which exerts much higher accuracy than baselines ($2.63\%$ increase in top-1 accuracy for MobileNetV1 with $90\%$ sparsity). As a result, our method demonstrates a more efficient dynamic convolution with sparsity.

本文提出了一种新框架Sparse Dynamic Convolution（SD-Conv），它将动态卷积和不规则剪枝结合起来，通过使用可学习阈值导出二值化掩码以减少参数和计算成本，在Imagenet-1K数据集上获得更高的性能，并在多个下游任务中展示出优于基线的表现，从而成为常规动态卷积的高效替代品。

SD-Conv： 动态卷积的参数效率

SD-Conv：动态卷积的参数效率