Unsupervised domain adaptive classification intends to improve theclassification performance on unlabeled target domain. To alleviate the adverse effect of domain shift, many approaches align the source and target domains in the feature space. However, a feature is usually taken as a whole for alignment without explicitly making domain alignment proactively serve the classification task, leading to sub-optimal solution. What sub-feature should be aligned for better adaptation is under-explored. In this paper, we propose an effective Task-oriented Alignment (ToAlign) for unsupervised domain adaptation (UDA). We study what features should be aligned across domains and propose to make the domain alignment proactively serve classification by performing feature decomposition and alignment under the guidance of the prior knowledge induced from the classification taskitself. Particularly, we explicitly decompose a feature in the source domain intoa task-related/discriminative feature that should be aligned, and a task-irrelevant feature that should be avoided/ignored, based on the classification meta-knowledge. Extensive experimental results on various benchmarks (e.g., Office-Home, Visda-2017, and DomainNet) under different domain adaptation settings demonstrate theeffectiveness of ToAlign which helps achieve the state-of-the-art performance.
翻译:不受监督的域适应性分类旨在改进未加标签的目标域域的分类性性能。 为了减轻域变的不利影响,许多方法在特性空间中调整源和目标域。 但是,通常将一个特性作为一个整体来调整,而没有在分类任务本身产生的先前知识的指导下进行特征分解和调整,而是主动地使域对齐,从而实现分类任务任务上的最佳解决办法。为了更好地适应,应当调整哪些次特性。在本文件中,我们建议为不受监督的域适应(UDA)提供一个有效的面向任务的对齐(To Align)办法。我们研究哪些特点应在各个域间统一,并建议在分类任务本身的先前知识指导下,通过执行特征分解和对齐来积极为分类服务。特别是,我们明确将源域内的一个特性分解成一个与任务有关/异性特点,这些特点应当统一起来,并根据分类元知识,避免/连接与任务有关的特性。我们研究各种基准(例如,Office-Home,Visda-2017,Domain)的广泛实验性结果,从而在不同的域内显示业绩的效能。