Deep residual networks were shown to be able to scale up to thousands of layers
and still have improving performance. However, each fraction of a percent of improved
accuracy costs nearly doubling the number of layers, and so training very deep residual networks has a problem of diminishing feature reuse, which makes these networks
very slow to train. To tackle these problems, in this paper we conduct a detailed experimental study on the architecture of ResNet blocks, based on which we propose a novel
architecture where we decrease depth and increase width of residual networks. We call
the resulting network structures wide residual networks (WRNs) and show that these are
far superior over their commonly used thin and very deep counterparts. For example,
we demonstrate that even a simple 16-layer-deep wide residual network outperforms in
accuracy and efficiency all previous deep residual networks, including thousand-layerdeep networks, achieving new state-of-the-art results on CIFAR, SVHN, COCO, and
significant improvements on ImageNet.
