CoDiNet: Path Distribution Modeling with Consistency and Diversity for Dynamic Routing
This work addresses dynamic routing for neural networks, offering a novel method to improve accuracy and efficiency, though it is incremental as it builds upon existing routing frameworks.
The paper tackles the problem of dynamic routing networks by proposing CoDiNet, which models the relationship between sample and routing spaces with consistency and diversity regularization, achieving higher performance and reduced computational cost on four datasets when deployed on ResNet models.
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.