Recent works imply that the channel pruning can be regarded as searching
optimal sub-structure from unpruned networks. However, existing works based on
this observation require training and evaluating a large number of structures,
which limits their application. In this paper, we propose a novel
differentiable method for channel pruning, named Differentiable Markov Channel
Pruning (DMCP), to efficiently search the optimal sub-structure. Our method is
differentiable and can be directly optimized by gradient descent with respect
to standard task loss and budget regularization (e.g. FLOPs constraint). In
DMCP, we model the channel pruning as a Markov process, in which each state
represents for retaining the corresponding channel during pruning, and
transitions between states denote the pruning process. In the end, our method
is able to implicitly select the proper number of channels in each layer by the
Markov process with optimized transitions. To validate the effectiveness of our
method, we perform extensive experiments on Imagenet with ResNet and
MobilenetV2. Results show our method can achieve consistent improvement than
state-of-the-art pruning methods in various FLOPs settings. The code is
available at this https URL

本文提出了一种基于马尔可夫过程的可微信道修剪方法 Differentiable Markov Channel Pruning (DMCP)，它可以通过梯度下降直接优化标准任务损失和预算规则来有效地搜索最优子结构，并可以在各种 FLOPs 设置中实现比现有最优修剪方法更为准确和优秀的结果。