  Due to the difficulty of obtaining ground-truth labels, learning from
virtual-world datasets is of great interest for real-world applications like
semantic segmentation. From domain adaptation perspective, the key challenge is
to learn domain-agnostic representation of the inputs in order to benefit from
virtual data. In this paper, we propose a novel trident-like architecture that
enforces a shared feature encoder to satisfy confrontational source and target
constraints simultaneously, thus learning a domain-invariant feature space.
Moreover, we also introduce a novel training pipeline enabling self-induced
cross-domain data augmentation during the forward pass. This contributes to a
further reduction of the domain gap. Combined with a self-training process, we
obtain state-of-the-art results on benchmark datasets (e.g. GTA5 or Synthia to
Cityscapes adaptation). Code and pre-trained models are available at
https://github.com/HMRC-AEL/TridentAdapt
