In this article, we take one step toward understanding the learning behavior
of deep residual networks, and supporting the observation that deep residual
networks behave like ensembles. We propose a new convolutional neural network
architecture which builds upon the success of residual networks by explicitly
exploiting the interpretation of very deep networks as an ensemble. The
proposed multi-residual network increases the number of residual functions in
the residual blocks. Our architecture generates models that are wider, rather
than deeper, which significantly improves accuracy. We show that our model
achieves an error rate of 3.73% and 19.45% on CIFAR-10 and CIFAR-100
respectively, that outperforms almost all of the existing models. We also
demonstrate that our model outperforms very deep residual networks by 0.22%
(top-1 error) on the full ImageNet 2012 classification dataset. Additionally,
inspired by the parallel structure of multi-residual networks, a model
parallelism technique has been investigated. The model parallelism method
distributes the computation of residual blocks among the processors, yielding
up to 15% computational complexity improvement.

本文提出了一种新的卷积神经网络架构，该架构通过扩展残差网络的层数，明确利用非常深网络的集成表现，并使模型变宽而不是更深，从而显著提高了准确性。受多元残差网络的并行结构启发，本研究还探讨了一种模型并行技术，该技术使残差块的计算分布在处理器之间，计算复杂度提高了 15%。最后，我们证明了我们模型在 ImageNet 分类数据集上优于大多数现有模型的表现。