  We introduce an unsupervised domain adaption (UDA) strategy that combines
multiple image translations, ensemble learning and self-supervised learning in
one coherent approach. We focus on one of the standard tasks of UDA in which a
semantic segmentation model is trained on labeled synthetic data together with
unlabeled real-world data, aiming to perform well on the latter. To exploit the
advantage of using multiple image translations, we propose an ensemble learning
approach, where three classifiers calculate their prediction by taking as input
features of different image translations, making each classifier learn
independently, with the purpose of combining their outputs by sparse
Multinomial Logistic Regression. This regression layer known as meta-learner
helps to reduce the bias during pseudo label generation when performing
self-supervised learning and improves the generalizability of the model by
taking into consideration the contribution of each classifier. We evaluate our
method on the standard UDA benchmarks, i.e. adapting GTA V and Synthia to
Cityscapes, and achieve state-of-the-art results in the mean intersection over
union metric. Extensive ablation experiments are reported to highlight the
advantageous properties of our proposed UDA strategy.
