In this paper, we study the formalism of unsupervised multi-class domain adaptation (multi-class UDA), which underlies a few recent algorithms whose learning objectives are only motivated empirically. Multi-Class Scoring Disagreement (MCSD) divergence is presented by aggregating the absolute margin violations in multi-class classification, and this proposed MCSD is able to fully characterize the relations between any pair of multi-class scoring hypotheses. By using MCSD as a measure of domain distance, we develop a new domain adaptation bound for multi-class UDA; its data-dependent, probably approximately correct bound is also developed that naturally suggests adversarial learning objectives to align conditional feature distributions across source and target domains. Consequently, an algorithmic framework of Multi-class Domain-adversarial learning Networks (McDalNets) is developed, and its different instantiations via surrogate learning objectives either coincide with or resemble a few recently popular methods, thus (partially) underscoring their practical effectiveness. Based on our identical theory for multi-class UDA, we also introduce a new algorithm of Domain-Symmetric Networks (SymmNets), which is featured by a novel adversarial strategy of domain confusion and discrimination. SymmNets affords simple extensions that work equally well under the problem settings of either closed set, partial, or open set UDA. We conduct careful empirical studies to compare different algorithms of McDalNets and our newly introduced SymmNets. Experiments verify our theoretical analysis and show the efficacy of our proposed SymmNets. In addition, we have made our implementation code publicly available.
翻译:在本文中,我们研究了未经监督的多级网络域适应(多级 UDA)的正规主义,这是最近几个算法的基础,其学习目标只是以经验为动机的。多级 Scorporation disagree (MCSD) 差异是通过在多级分类中汇总绝对侵犯差数(McDals ) 来呈现的,而这个拟议的 MCSD 能够充分描述任何一对多级评分假设之间的关系。通过将MCSD作为域距离的衡量标准,我们为多级 UDA 开发了一个新的域网域适应;数据依赖性(可能大致正确)也得到了开发,这自然表明对称学习目标的对立性目标,以调源和目标域的有条件地分布。因此,多级多级的多级对立性学习网络(McDals) 的算法框架正在形成, 其通过代号学习目标或类似于最近几个受欢迎的方法, 来强调它们的实际有效性。根据我们现有的多级 UDA理论, 我们还引入了一种新的Sy-SySySy-Syalalalalalaldealal Net的算法则, 我们的精确的系统域域域域块的理论, 提供了一个新的的固定化的模型的模型的模型的模型的模型的模型, 。