Filter pruning and low-rank decomposition are two of the foundational
techniques for structured compression. Although recent efforts have explored
hybrid approaches aiming to integrate the advantages of both techniques, their
performance gains have been modest at best. In this study, we develop a
\textit{Differentiable Framework~(DF)} that can express filter selection, rank
selection, and budget constraint into a single analytical formulation. Within
the framework, we introduce DML-S for filter selection, integrating scheduling
into existing mask learning techniques. Additionally, we present DTL-S for rank
selection, utilizing a singular value thresholding operator. The framework with
DML-S and DTL-S offers a hybrid structured compression methodology that
facilitates end-to-end learning through gradient-base optimization.
Experimental results demonstrate the efficacy of DF, surpassing
state-of-the-art structured compression methods. Our work establishes a robust
and versatile avenue for advancing structured compression techniques.

本研究展示了一种可表达滤波器选择、秩选择和预算约束的可微分框架（DF），其中利用 DML-S 进行滤波器选择，将调度集成到现有的掩模学习技术中，并利用 DTL-S 进行秩选择。该框架及其组件通过基于梯度的优化实现端到端学习，实验结果显示 DF 的有效性超过了最先进的结构化压缩方法，为推动结构化压缩技术提供了一个强大而多功能的途径。