  The effective receptive field of a fully convolutional neural network is an
important consideration when designing an architecture, as it defines the
portion of the input visible to each convolutional kernel. We propose a neural
network module, extending traditional skip connections, called the translated
skip connection. Translated skip connections geometrically increase the
receptive field of an architecture with negligible impact on both the size of
the parameter space and computational complexity. By embedding translated skip
connections into a benchmark architecture, we demonstrate that our module
matches or outperforms four other approaches to expanding the effective
receptive fields of fully convolutional neural networks. We confirm this result
across five contemporary image segmentation datasets from disparate domains,
including the detection of COVID-19 infection, segmentation of aerial imagery,
common object segmentation, and segmentation for self-driving cars.
