  Deep supervised models have an unprecedented capacity to absorb large
quantities of training data. Hence, training on many datasets becomes a method
of choice towards graceful degradation in unusual scenes. Unfortunately,
different datasets often use incompatible labels. For instance, the Cityscapes
road class subsumes all driving surfaces, while Vistas defines separate classes
for road markings, manholes etc. We address this challenge by proposing a
principled method for seamless learning on datasets with overlapping classes
based on partial labels and probabilistic loss. Our method achieves competitive
within-dataset and cross-dataset generalization, as well as ability to learn
visual concepts which are not separately labeled in any of the training
datasets. Experiments reveal competitive or state-of-the-art performance on two
multi-domain dataset collections and on the WildDash 2 benchmark.
