论文标题
揭示通用域适应的类标记结构
Unveiling Class-Labeling Structure for Universal Domain Adaptation
论文作者
论文摘要
作为无监督域适应性的更实际的设置，最近引入了通用域适应（UDA），其中目标标签集尚不清楚。 UDA中最大的挑战之一是如何确定由源和目标域共享的通用标签集，因为目标域中根本没有标签。在本文中，我们采用了一种概率方法来定位通用标签集，其中每个源类可能来自具有概率的通用标签集。特别是，我们提出了一种新的方法，用于评估来自共同标签集的每个源类别的概率，在该集合集中，该概率是由在整个目标域上积累的预测余量计算得出的。然后，我们通过结合共同标签集的概率结构来提出一个简单的通用适应网络（S-UAN）。最后，我们分析着关注通用标签集的概括性结合，并探索UDA目标风险的属性。广泛的实验表明，S-UAN在不同的UDA设置中运作良好，并以大边距优于最先进的方法。
As a more practical setting for unsupervised domain adaptation, Universal Domain Adaptation (UDA) is recently introduced, where the target label set is unknown. One of the big challenges in UDA is how to determine the common label set shared by source and target domains, as there is simply no labeling available in the target domain. In this paper, we employ a probabilistic approach for locating the common label set, where each source class may come from the common label set with a probability. In particular, we propose a novel approach for evaluating the probability of each source class from the common label set, where this probability is computed by the prediction margin accumulated over the whole target domain. Then, we propose a simple universal adaptation network (S-UAN) by incorporating the probabilistic structure for the common label set. Finally, we analyse the generalization bound focusing on the common label set and explore the properties on the target risk for UDA. Extensive experiments indicate that S-UAN works well in different UDA settings and outperforms the state-of-the-art methods by large margins.
