Current adversarial adaptation methods attempt to align the cross-domain features, whereas two challenges remain unsolved: 1) the conditional distribution mismatch and 2) the bias of the decision boundary towards the source domain. To solve these challenges, we propose a novel framework for semi-supervised domain adaptation by unifying the learning of opposite structures (UODA). UODA consists of a generator and two classifiers (i.e., the source-scattering classifier and the target-clustering classifier), which are trained for contradictory purposes. The target-clustering classifier attempts to cluster the target features to improve intra-class density and enlarge inter-class divergence. Meanwhile, the source-scattering classifier is designed to scatter the source features to enhance the decision boundary's smoothness. Through the alternation of source-feature expansion and target-feature clustering procedures, the target features are well-enclosed within the dilated boundary of the corresponding source features. This strategy can make the cross-domain features to be precisely aligned against the source bias simultaneously. Moreover, to overcome the model collapse through training, we progressively update the measurement of feature's distance and their representation via an adversarial training paradigm. Extensive experiments on the benchmarks of DomainNet and Office-home datasets demonstrate the superiority of our approach over the state-of-the-art methods.

翻译：当前的对抗性适应方法试图调整跨主题特征,而两个挑战仍未解决:(1) 有条件的分布错配和(2) 决定界限对源域的偏差。为了应对这些挑战,我们提议了一个半监督域调整新框架,通过统一对对对立结构的学习(UDA),我们提议了一个半监督域调整新框架。UDA由一个发电机和两个分类器(即源排散分类器和目标分组分类器)组成,这些分类器经过了相互矛盾的训练。目标分组分类器试图将目标特征集中起来,以提高阶级内部密度和扩大阶级间差异。与此同时,源排挤分类器的设计是为了分散源特性,以提高决定边界的光滑度。通过源性扩展和具体目标组合程序的改变,目标特征完全封闭在相应源特性的较晚边界内。这一战略可以使交叉特性与源的偏差同时保持一致。此外,通过培训,我们逐步更新对地位距离和网络代表性模型的测量,并通过对质性培训展示我们区域基准的模型。