  DenseNets have been shown to be a competitive model among recent
convolutional network architectures. These networks utilize Dense Blocks, which
are groups of densely connected layers where the output of a hidden layer is
fed in as the input of every other layer following it. In this paper, we aim to
improve certain aspects of DenseNet, especially when it comes to practicality.
We introduce ParaNet, a new architecture that constructs three pipelines which
allow for early inference. We additionally introduce a cascading mechanism such
that different pipelines are able to share parameters, as well as logit
matching between the outputs of the pipelines. We separately evaluate each of
the newly introduced mechanisms of ParaNet, then evaluate our proposed
architecture on CIFAR-100.
