CoDiNet: Path Distribution Modeling With Consistency and Diversity for Dynamic Routing

• Published in: IEEE transactions on pattern analysis and machine intelligence 2022-10, Vol.44 (10), p.6011-6023
• Main Authors: Wang, Huanyu, Qin, Zequn, Li, Songyuan, Li, Xi
• Format: Article
• Language: English
• Subjects: Accuracy; Computational efficiency; Computational modeling; Consistency; distribution of routing paths; dynamic routing; Image color analysis; Mapping; Neural networks; Predictive models; Recurrent neural networks; Routing; Routing space mapping; the consistency regularization; the diversity regularization; Training
• ISSN: 0162-8828; 1939-3539; 2160-9292
• DOI: 10.1109/TPAMI.2021.3084680

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 did not take into account how routing 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. In principle, the routing paths for the self-supervised similar samples should be closely distributed in the routing space. 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.