  Adversarial learning baselines for domain adaptation (DA) approaches in the
context of semantic segmentation are under explored in semi-supervised
framework. These baselines involve solely the available labeled target samples
in the supervision loss. In this work, we propose to enhance their usefulness
on both semantic segmentation and the single domain classifier neural networks.
We design new training objective losses for cases when labeled target data
behave as source samples or as real target samples. The underlying rationale is
that considering the set of labeled target samples as part of source domain
helps reducing the domain discrepancy and, hence, improves the contribution of
the adversarial loss. To support our approach, we consider a complementary
method that mixes source and labeled target data, then applies the same
adaptation process. We further propose an unsupervised selection procedure
using entropy to optimize the choice of labeled target samples for adaptation.
We illustrate our findings through extensive experiments on the benchmarks
GTA5, SYNTHIA, and Cityscapes. The empirical evaluation highlights competitive
performance of our proposed approach.
