  Skip connections are increasingly utilized by deep neural networks to improve
accuracy and cost-efficiency. In particular, the recent DenseNet is efficient
in computation and parameters, and achieves state-of-the-art predictions by
directly connecting each feature layer to all previous ones. However,
DenseNet's extreme connectivity pattern may hinder its scalability to high
depths, and in applications like fully convolutional networks, full DenseNet
connections are prohibitively expensive. This work first experimentally shows
that one key advantage of skip connections is to have short distances among
feature layers during backpropagation. Specifically, using a fixed number of
skip connections, the connection patterns with shorter backpropagation distance
among layers have more accurate predictions. Following this insight, we propose
a connection template, Log-DenseNet, which, in comparison to DenseNet, only
slightly increases the backpropagation distances among layers from 1 to ($1 +
\log_2 L$), but uses only $L\log_2 L$ total connections instead of $O(L^2)$.
Hence, Log-DenseNets are easier than DenseNets to implement and to scale. We
demonstrate the effectiveness of our design principle by showing better
performance than DenseNets on tabula rasa semantic segmentation, and
competitive results on visual recognition.
