SP-Net: Slowly Progressing Dynamic Inference Networks
Huanyu Wang, Wenhu Zhang, Shihao Su, Hui Wang, Zhenwei Miao, Xin Zhan, Xi Li
"Dynamic inference networks improve computational efficiency by executing a subset of network components, i.e., executing path, conditioned on input sample. Prevalent methods typically assign routers to computational blocks so that a computational block can be skipped or executed. However, such inference mechanisms are prone to suffer instability in the optimization of dynamic inference networks. First, a dynamic inference network is more sensitive to its routers than its computational blocks. Second, the components executed by the network vary with samples, resulting in unstable feature evolution throughout the network. To alleviate the problems above, we propose a slowly progressing dynamic inference network to stabilize the optimization. First, we design a dynamic auxiliary module to slow down the progress in routers. Moreover, we regularize the feature evolution directions across the network to smoothen the feature extraction. As a result, we conduct extensive experiments on three widely used benchmarks and show that our proposed SP-Nets achieve state-of-the-art performance in terms of efficiency and accuracy."