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Residual Neural Networks [1] won first place in all five main tracks of theImageNet and COCO 2015 competitions. This kind of network involves the creationof pluggable modules such that the output contains a residual from the input.The residual in that paper is the identity function. We propose to includeresiduals from all lower layers, suitably normalized, to create the residual.This way, all previous layers contribute equally to the output of a layer. Weshow that our approach is an improvement on [1] for the CIFAR-10 dataset.

Using accumulation to optimize deep residual neural nets