  Designing efficient network structures has always been the core content of
neural network research.
  ResNet and its variants have proved to be efficient in architecture.
  However, how to theoretically character the influence of network structure on
performance is still vague.
  With the help of techniques in complex networks, We here provide a natural
yet efficient extension to ResNet by folding its backbone chain.
  Our architecture has two structural features when being mapped to directed
acyclic graphs:
  First is a higher degree of the disorder compared with ResNet, which let
ResNetX explore a larger number of feature maps with different sizes of
receptive fields.
  Second is a larger proportion of shorter paths compared to ResNet, which
improves the direct flow of information through the entire network.
  Our architecture exposes a new dimension, namely "fold depth", in addition to
existing dimensions of depth, width, and cardinality.
  Our architecture is a natural extension to ResNet, and can be integrated with
existing state-of-the-art methods with little effort. Image classification
results on CIFAR-10 and CIFAR-100 benchmarks suggested that our new network
architecture performs better than ResNet.
