  Recent researches on unsupervised domain adaptation (UDA) have demonstrated
that end-to-end ensemble learning frameworks serve as a compelling option for
UDA tasks. Nevertheless, these end-to-end ensemble learning methods often lack
flexibility as any modification to the ensemble requires retraining of their
frameworks. To address this problem, we propose a flexible
ensemble-distillation framework for performing semantic segmentation based UDA,
allowing any arbitrary composition of the members in the ensemble while still
maintaining its superior performance. To achieve such flexibility, our
framework is designed to be robust against the output inconsistency and the
performance variation of the members within the ensemble. To examine the
effectiveness and the robustness of our method, we perform an extensive set of
experiments on both GTA5 to Cityscapes and SYNTHIA to Cityscapes benchmarks to
quantitatively inspect the improvements achievable by our method. We further
provide detailed analyses to validate that our design choices are practical and
beneficial. The experimental evidence validates that the proposed method indeed
offer superior performance, robustness and flexibility in semantic segmentation
based UDA tasks against contemporary baseline methods.
