Abstract
Classical deep convolutional networks increase receptive field size by either gradual resolution reduction or application of hand-crafted dilated convolutions to prevent increase in the number of parameters. In this paper we propose a novel displaced aggregation unit (DAU) that does
not require hand-crafting. In contrast to classical filters
with units (pixels) placed on a fixed regular grid, the displacement of the DAUs are learned, which enables filters
to spatially-adapt their receptive field to a given problem.
We extensively demonstrate the strength of DAUs on a classification and semantic segmentation tasks. Compared to
ConvNets with regular filter, ConvNets with DAUs achieve
comparable performance at faster convergence and up to
3-times reduction in parameters. Furthermore, DAUs allow
us to study deep networks from novel perspectives. We study
spatial distributions of DAU filters and analyze the number
of parameters allocated for spatial coverage in a filter.