论文标题
使用基于蒸馏的通道修剪的轻量级α效果网络
Lightweight Alpha Matting Network Using Distillation-Based Channel Pruning
论文作者
论文摘要
最近，由于Alpha Matting在自拍照等移动应用中的有用性，因此受到了很多关注。因此，由于商业便携式设备的计算资源有限，因此需求对轻量级的alpha矩阵模型。为此，我们建议针对Alpha Matting网络进行基于蒸馏的通道修剪方法。在修剪步骤中，我们删除学生网络的渠道，对模仿教师网络的知识的影响较少。然后，修剪的轻量级学生网络通过相同的蒸馏损失进行培训。提出的方法的轻量级α矩阵模型优于现有的轻量级方法。为了显示我们算法的优越性，我们通过深入分析提供了各种定量和定性实验。此外，我们通过将基于蒸馏的通道修剪方法应用于语义分割来证明所提出的基于蒸馏的通道修剪方法的多功能性。
Recently, alpha matting has received a lot of attention because of its usefulness in mobile applications such as selfies. Therefore, there has been a demand for a lightweight alpha matting model due to the limited computational resources of commercial portable devices. To this end, we suggest a distillation-based channel pruning method for the alpha matting networks. In the pruning step, we remove channels of a student network having fewer impacts on mimicking the knowledge of a teacher network. Then, the pruned lightweight student network is trained by the same distillation loss. A lightweight alpha matting model from the proposed method outperforms existing lightweight methods. To show superiority of our algorithm, we provide various quantitative and qualitative experiments with in-depth analyses. Furthermore, we demonstrate the versatility of the proposed distillation-based channel pruning method by applying it to semantic segmentation.
