  Dilated Convolutions have been shown to be highly useful for the task of
image segmentation. By introducing gaps into convolutional filters, they enable
the use of larger receptive fields without increasing the original kernel size.
Even though this allows for the inexpensive capturing of features at different
scales, the structure of the dilated convolutional filter leads to a loss of
information. We hypothesise that inexpensive modifications to Dilated
Convolutional Neural Networks, such as additional averaging layers, could
overcome this limitation. In this project we test this hypothesis by evaluating
the effect of these modifications for a state-of-the art image segmentation
system and compare them to existing approaches with the same objective. Our
experiments show that our proposed methods improve the performance of dilated
convolutions for image segmentation. Crucially, our modifications achieve these
results at a much lower computational cost than previous smoothing approaches.
