  Recent success on realistic road driving datasets has increased interest in
exploring robust performance in real-world applications. One of the major
unsolved problems is to identify image content which can not be reliably
recognized with a given inference engine. We therefore study approaches to
recover a dense outlier map alongside the primary task with a single forward
pass, by relying on shared convolutional features. We consider semantic
segmentation as the primary task and perform extensive validation on WildDash
val (inliers), LSUN val (outliers), and pasted objects from Pascal VOC 2007
(outliers). We achieve the best validation performance by training to
discriminate inliers from pasted ImageNet-1k content, even though ImageNet-1k
contains many road-driving pixels, and, at least nominally, fails to account
for the full diversity of the visual world. The proposed two-head model
performs comparably to the C-way multi-class model trained to predict uniform
distribution in outliers, while outperforming several other validated
approaches. We evaluate our best two models on the WildDash test dataset and
set a new state of the art on the WildDash benchmark.
