In this paper, we study the formalism of unsupervised multi-class domain adaptation (multi-class UDA), which underlies some recent algorithms whose learning objectives are only motivated empirically. A Multi-Class Scoring Disagreement (MCSD) divergence is presented by aggregating the absolute margin violations in multi-class classification; the 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 as well as its data-dependent, probably approximately correct bound, which naturally suggest adversarial learning objectives to align conditional feature distributions across the source and target domains. Consequently, an algorithmic framework of Multi-class Domain-adversarial learning Networks (McDalNets) is developed, whose different instantiations via surrogate learning objectives either coincide with or resemble a few of recently popular methods, thus (partially) underscoring their practical effectiveness. Based on our same theory of 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 afford 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. We make our implementation codes publicly available.

本文介绍了一种基于MCSD的多类域间自适应的学习方法(Multi-class Domain-adversarial learning Networks （McDalNets）), 通过使用数据相关的 PAC 常数, 研究了其有效性, 并引入了结合了领域混淆和歧视的新型敌对策略的域对称网络（SymmNets）来提高多类域间自适应性能。

无监督多类域自适应：理论、算法和实践