Semantic segmentation models trained on annotated data fail to generalize well when the input data distribution changes over extended time period, leading to requiring re-training to maintain performance. Classic Unsupervised domain adaptation (UDA) attempts to address a similar problem when there is target domain with no annotated data points through transferring knowledge from a source domain with annotated data. We develop an online UDA algorithm for semantic segmentation of images that improves model generalization on unannotated domains in scenarios where source data access is restricted during adaptation. We perform model adaptation is by minimizing the distributional distance between the source latent features and the target features in a shared embedding space. Our solution promotes a shared domain-agnostic latent feature space between the two domains, which allows for classifier generalization on the target dataset. To alleviate the need of access to source samples during adaptation, we approximate the source latent feature distribution via an appropriate surrogate distribution, in this case a Gassian mixture model (GMM). We evaluate our approach on well established semantic segmentation datasets and demonstrate it compares favorably against state-of-the-art (SOTA) UDA semantic segmentation methods.

翻译：在标注数据上训练的语义分割模型，当输入数据分布随时间发生较大变化时，泛化能力会下降，因此需要重新训练以维持性能。经典的無监督域适应（UDA）通过从有标注数据的源域迁移知识，试图解决目标域无标注数据点时的类似问题。我们提出了一种针对图像语义分割的在线UDA算法，该算法在适应过程中源数据访问受限的场景下，提升了模型在无标注域上的泛化能力。我们通过最小化共享嵌入空间中源潜在特征与目标特征之间的分布距离来进行模型适应。我们的方法促进了两域之间共享一个与域无关的潜在特征空间，从而实现了分类器在目标数据集上的泛化。为了缓解适应过程中对源样本访问的需求，我们通过合适的代理分布（此处为高斯混合模型GMM）近似源潜在特征分布。我们在公认的语义分割数据集上评估了该方法，并证明其性能优于现有的最先进（SOTA）UDA语义分割方法。