Multi-Residual Networks

In this article, we take one step toward understanding the learning behavior of deep residual networks, and supporting the hypothesis that deep residual networks are exponential ensembles by construction. We examine the effective range of ensembles by introducing multi-residual networks that significantly improve classification accuracy of residual networks. The multi-residual networks increase the number of residual functions in the residual blocks. This is shown to improve the accuracy of the residual network when the network is deeper than a threshold. Based on a series of empirical studies on CIFAR-10 and CIFAR-100 datasets, the proposed multi-residual network yield $6\%$ and $10\%$ improvement with respect to the residual networks with identity mappings. Comparing with other state-of-the-art models, the proposed multi-residual network obtains a test error rate of $3.92\%$ on CIFAR-10 that outperforms all existing models.