  In this work we challenge the common approach of using a one-to-one mapping
('translation') between the source and target domains in unsupervised domain
adaptation (UDA). Instead, we rely on stochastic translation to capture
inherent translation ambiguities. This allows us to (i) train more accurate
target networks by generating multiple outputs conditioned on the same source
image, leveraging both accurate translation and data augmentation for
appearance variability, (ii) impute robust pseudo-labels for the target data by
averaging the predictions of a source network on multiple translated versions
of a single target image and (iii) train and ensemble diverse networks in the
target domain by modulating the degree of stochasticity in the translations. We
report improvements over strong recent baselines, leading to state-of-the-art
UDA results on two challenging semantic segmentation benchmarks. Our code is
available at
https://github.com/elchiou/Beyond-deterministic-translation-for-UDA.
