  The trend towards increasingly deep neural networks has been driven by a
general observation that increasing depth increases the performance of a
network. Recently, however, evidence has been amassing that simply increasing
depth may not be the best way to increase performance, particularly given other
limitations. Investigations into deep residual networks have also suggested
that they may not in fact be operating as a single deep network, but rather as
an ensemble of many relatively shallow networks. We examine these issues, and
in doing so arrive at a new interpretation of the unravelled view of deep
residual networks which explains some of the behaviours that have been observed
experimentally. As a result, we are able to derive a new, shallower,
architecture of residual networks which significantly outperforms much deeper
models such as ResNet-200 on the ImageNet classification dataset. We also show
that this performance is transferable to other problem domains by developing a
semantic segmentation approach which outperforms the state-of-the-art by a
remarkable margin on datasets including PASCAL VOC, PASCAL Context, and
Cityscapes. The architecture that we propose thus outperforms its comparators,
including very deep ResNets, and yet is more efficient in memory use and
sometimes also in training time. The code and models are available at
https://github.com/itijyou/ademxapp
