Typical adversarial-training-based unsupervised domain adaptation methods are vulnerable when the source and target datasets are highly-complex or exhibit a large discrepancy between their data distributions. Recently, several Lipschitz-constraint-based methods have been explored. The satisfaction of Lipschitz continuity guarantees a remarkable performance on a target domain. However, they lack a mathematical analysis of why a Lipschitz constraint is beneficial to unsupervised domain adaptation and usually perform poorly on large-scale datasets. In this paper, we take the principle of utilizing a Lipschitz constraint further by discussing how it affects the error bound of unsupervised domain adaptation. A connection between them is built and an illustration of how Lipschitzness reduces the error bound is presented. A \textbf{local smooth discrepancy} is defined to measure Lipschitzness of a target distribution in a pointwise way. When constructing a deep end-to-end model, to ensure the effectiveness and stability of unsupervised domain adaptation, three critical factors are considered in our proposed optimization strategy, i.e., the sample amount of a target domain, dimension and batchsize of samples. Experimental results demonstrate that our model performs well on several standard benchmarks. Our ablation study shows that the sample amount of a target domain, the dimension and batchsize of samples indeed greatly impact Lipschitz-constraint-based methods' ability to handle large-scale datasets. Code is available at https://github.com/CuthbertCai/SRDA.
翻译:当源和目标数据集高度复杂或显示其数据分布存在巨大差异时,典型的对抗性培训、不受监督的典型域适应方法就很脆弱。最近,探索了几种基于Lipschitz-constraint的方法。Lipschitz连续性的满意度保证了目标域的显著性能。然而,它们缺乏对利普申茨制约为何有利于不受监督域适应并通常在大型数据集上表现不佳的数学分析。在本文件中,我们进一步考虑使用利普施茨限制的原则,讨论它如何影响不受监督域适应的错误。建立了它们之间的连接,并展示了利普施茨特尼特如何减少错误的束缚。利普施茨连续性的满意度保证了在目标域上的显著性表现。利普申茨限制对于利普申茨的制约性,在构建一个深端至端的域适应模型时,我们的拟议优化战略中考虑了三个关键因素,即目标域域域的抽样量,在目标域域域、层面和分级的标度上展示了我们现有标准标度的标度标度。