Deep convolutional neural networks for semantic segmentation achieve outstanding accuracy, however they also have a couple of major drawbacks: first, they do not generalize well to distributions slightly different from the one of the training data; second, they require a huge amount of labeled data for their optimization. In this paper, we introduce feature-level space-shaping regularization strategies to reduce the domain discrepancy in semantic segmentation. In particular, for this purpose we jointly enforce a clustering objective, a perpendicularity constraint and a norm alignment goal on the feature vectors corresponding to source and target samples. Additionally, we propose a novel measure able to capture the relative efficacy of an adaptation strategy compared to supervised training. We verify the effectiveness of such methods in the autonomous driving setting achieving state-of-the-art results in multiple synthetic-to-real road scenes benchmarks.

翻译：深卷动神经网络的语义分解的准确性非常突出,但它们也有一些重大缺点:第一,它们没有很好地概括到与培训数据略有不同的分布;第二,它们需要大量贴标签的数据才能优化。在本文件中,我们引入了地平层空间整形战略,以减少语义分解的域差异。特别是,为此目的,我们共同实施一个集群目标、一个孔径限制和与源和目标样本相对应的特征矢量的规范调整目标。此外,我们提议了一项新颖的措施,能够捕捉适应战略与监督培训相比的相对效力。我们核查这些方法在自主驾驶环境中的有效性,在多个合成到现实的道路场景基准中实现最新的结果。