Many unsupervised domain adaptation (UDA) methods have been proposed to bridge the domain gap by utilizing domain invariant information. Most approaches have chosen depth as such information and achieved remarkable success. Despite their effectiveness, using depth as domain invariant information in UDA tasks may lead to multiple issues, such as excessively high extraction costs and difficulties in achieving a reliable prediction quality. As a result, we introduce Edge Learning based Domain Adaptation (ELDA), a framework which incorporates edge information into its training process to serve as a type of domain invariant information. In our experiments, we quantitatively and qualitatively demonstrate that the incorporation of edge information is indeed beneficial and effective and enables ELDA to outperform the contemporary state-of-the-art methods on two commonly adopted benchmarks for semantic segmentation based UDA tasks. In addition, we show that ELDA is able to better separate the feature distributions of different classes. We further provide an ablation analysis to justify our design decisions.
翻译:提出了许多未经监督的域适应方法,以利用域变化中的信息缩小域间差距。大多数方法选择了深度作为这种信息,并取得了显著的成功。尽管这些方法具有效力,但将深度作为域变化中的变化中的信息,在UDA任务中可能会导致多种问题,如提取成本过高和难以实现可靠的预测质量。结果,我们引入了以边缘学习为基础的域适应(ELDA),这个框架将边际信息纳入其培训过程,作为域变化中的信息类型。我们在实验中从数量和质量上证明,纳入边际信息确实有益和有效,并使ELDA能够在基于UDA任务的两个共同采用的语义分解基准上超越当代最新方法。此外,我们表明ELDA能够更好地区分不同类别的特点分布。我们进一步提供了一种对比分析,以证明我们设计决定的合理性。