Dynamic Routing with Path Diversity and Consistency for Compact Network Learning

In this paper, we propose a novel dynamic routing inference method with diversity and consistency that better takes advantage of the network capacity. Specifically, by diverse routing, we achieve the goal of better utilizing of the network. By consistent routing, the better optimization of the routing mechanism is realized. Moreover, we propose a customizable computational cost controlling method that could balance the trade-off between cost and accuracy. Extensive ablation studies and experiments show that our method could achieve state-of-the-art results compared with the original full network, other dynamic networks and model compression methods. Our code will be made publicly available.