Efficient Channel Pruning Based on Architecture Alignment and Probability Model Bypassing

Lvfang Tao, Wei Gao

Abstract: In recent years, automated channel pruning methods have been proposed to search optimal sub-networks from cumbersome neural network models. Among all the efforts, differentiable methods stand out as an efficient solution to the substructure search problem. However, the early differentiable channel pruning method relies on a predefined probability model, which implicitly poses an obstacle to the optimization. Moreover, the importance of architecture alignment across the bi-level optimization may be underestimated. In this paper, a novel search algorithm for the optimal pruned model is proposed by overcoming the aforementioned deficiencies. Follow-up experiments demonstrate the superiority of the proposed method in terms of efficiency and performance.