论文标题
CodInet：具有一致性和多样性的路径分布建模，用于动态路由
CoDiNet: Path Distribution Modeling with Consistency and Diversity for Dynamic Routing
论文作者
论文摘要
旨在在网络中找到最佳路由路径的动态路由网络在准确性和效率方面已取得了重大改善。在本文中，我们在新鲜的光线下看到动态路由网络，将路由方法制定为从样本空间到路由空间的映射。从空间映射的角度来看，动态路由的普遍方法没有考虑如何将推理路径分布在路由空间中。因此，我们提出了一种称为CodInet的新方法，以通过将路由路径的分布与一致性和多样性的属性定向，以模拟样本空间和路由空间之间的关系。具体而言，具有相似语义的样品应映射到路由空间中的同一区域，而具有不同语义的样品应映射到不同的区域。此外，我们设计了一个可自定义的动态路由模块，可以在准确性和效率之间取得平衡。当在Resnet模型上部署时，我们的方法可实现更高的性能，并有效地降低了四个广泛使用的数据集的平均计算成本。
Dynamic routing networks, aimed at finding the best routing paths in the networks, have achieved significant improvements to neural networks in terms of accuracy and efficiency. In this paper, we see dynamic routing networks in a fresh light, formulating a routing method as a mapping from a sample space to a routing space. From the perspective of space mapping, prevalent methods of dynamic routing didn't consider how inference paths would be distributed in the routing space. Thus, we propose a novel method, termed CoDiNet, to model the relationship between a sample space and a routing space by regularizing the distribution of routing paths with the properties of consistency and diversity. Specifically, samples with similar semantics should be mapped into the same area in routing space, while those with dissimilar semantics should be mapped into different areas. Moreover, we design a customizable dynamic routing module, which can strike a balance between accuracy and efficiency. When deployed upon ResNet models, our method achieves higher performance and effectively reduces average computational cost on four widely used datasets.
