title: Building effective deep neural networks one feature at a time
abstract: Successful training of convolutional neural networks is often associated with suffi-
  ciently deep architectures composed of high amounts of features. These networks
  typically rely on a variety of regularization and pruning techniques to converge
  to less redundant states. We introduce a novel bottom-up approach to expand
  representations in fixed-depth architectures. These architectures start from just a
  single feature per layer and greedily increase width of individual layers to attain
  effective representational capacities needed for a specific task. While network
  growth can rely on a family of metrics, we propose a computationally efficient
  version based on feature time evolution and demonstrate its potency in determin-
  ing feature importance and a networks’ effective capacity. We demonstrate how
  automatically expanded architectures converge to similar topologies that benefit
  from lesser amount of parameters or improved accuracy and exhibit systematic
  correspondence in representational complexity with the specified task. In contrast
  to conventional design patterns with a typical monotonic increase in the amount of
  features with increased depth, we observe that CNNs perform better when there is
  more learnable parameters in intermediate, with falloffs to earlier and later layers.
authors: Martin Mundt Tobias Weis Kishore Konda Visvanathan Ramesh
hIndex: Visvanathan Ramesh 32