  Convolutional networks for image classification progressively reduce
resolution until the image is represented by tiny feature maps in which the
spatial structure of the scene is no longer discernible. Such loss of spatial
acuity can limit image classification accuracy and complicate the transfer of
the model to downstream applications that require detailed scene understanding.
These problems can be alleviated by dilation, which increases the resolution of
output feature maps without reducing the receptive field of individual neurons.
We show that dilated residual networks (DRNs) outperform their non-dilated
counterparts in image classification without increasing the model's depth or
complexity. We then study gridding artifacts introduced by dilation, develop an
approach to removing these artifacts (`degridding'), and show that this further
increases the performance of DRNs. In addition, we show that the accuracy
advantage of DRNs is further magnified in downstream applications such as
object localization and semantic segmentation.
