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

翻译：由于难以获得地面真相标签,从虚拟世界数据集中学习对于像语义分割等真实世界应用非常感兴趣。 从域适应角度,关键的挑战是如何学习输入的域-不可知性代表,以便从虚拟数据中受益。在本文件中,我们建议建立一个新型的三叉戟类似结构,同时执行一个共同的特征编码器,以满足对抗源和目标制约,从而学习一个域-异性特征空间。此外,我们还引入了一个新的培训管道,使在远行过程中能够自行引发跨界域数据增强。这有助于进一步缩小域间差距。与自我培训过程相结合,我们获得了基准数据集方面的最先进的结果(例如,GTA5或Synthia,以适应城市)。代码和预先培训的模式可在https://github.com/HMRC-AEL/TrientAdap查阅。