  We introduce a new architecture called ChoiceNet where each layer of the
network is highly connected with skip connections and channelwise
concatenations. This enables the network to alleviate the problem of vanishing
gradients, reduces the number of parameters without sacrificing performance,
and encourages feature reuse. We evaluate our proposed architecture on three
benchmark datasets for object recognition tasks (ImageNet, CIFAR- 10,
CIFAR-100, SVHN) and on a semantic segmentation dataset (CamVid).
