DMCP: Differentiable Markov Channel Pruning for Neural Networks

Understand

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.