  Scene understanding and semantic segmentation are at the core of many
computer vision tasks, many of which, involve interacting with humans in
potentially dangerous ways. It is therefore paramount that techniques for
principled design of robust models be developed. In this paper, we provide
analytic and empirical evidence that correcting potentially errant non-distinct
mappings that result from the softmax function can result in improving
robustness characteristics on a state-of-the-art semantic segmentation model
with minimal impact to performance and minimal changes to the code base.
