Deep learning frameworks allowed for a remarkable advancement in semantic segmentation, but the data hungry nature of convolutional networks has rapidly raised the demand for adaptation techniques able to transfer learned knowledge from label-abundant domains to unlabeled ones. In this paper we propose an effective Unsupervised Domain Adaptation (UDA) strategy, based on a feature clustering method that captures the different semantic modes of the feature distribution and groups features of the same class into tight and well-separated clusters. Furthermore, we introduce two novel learning objectives to enhance the discriminative clustering performance: an orthogonality loss forces spaced out individual representations to be orthogonal, while a sparsity loss reduces class-wise the number of active feature channels. The joint effect of these modules is to regularize the structure of the feature space. Extensive evaluations in the synthetic-to-real scenario show that we achieve state-of-the-art performance.

翻译：深度学习框架允许在语义分割方面取得显著进步,但数据饥饿的性质使得革命网络的数据饥饿性迅速增加了对适应技术的需求,这些技术能够将从标签丰富领域学到的知识转移至无标签领域。在本文件中,我们提出一个有效的不受监督的域域适应(UDA)战略,其基础是将同一类特征分布的不同语义模式和群落特征分成紧凑和分离的集群。此外,我们引入了两个新颖的学习目标,以加强歧视性的集群性能:一个或多位性损失迫使个人外表显示成正方形,而宽度损失则减少主动地貌渠道的数量。这些模块的共同效果是将地貌空间的结构正规化。合成到真实情景中的广泛评估显示我们实现了最先进的性能。