Abstract Unsupervised domain adaptation is an effective approach to solve the problem of dataset bias. However, most existing unsupervised domain adaptation methods assume that the geometry structures of data distributions are similar in the source and target domains. This assumption is invalid in many practical applications, because the training and test datasets usually differ in the variability modes and/or variation degrees. This paper handles the problem of inconsistent geometries by aligning both data representations and geometries. To overcome the lack of target labels in aligning geometries, this paper proposes learning the adaptive geometry that is derived from the domain-shared label space. Source and target geometries are aligned by constraining them with the unified criteria of the adaptive geometry. Combining the adaptive geometry learning and adversarial learning techniques, we develop a geometry-aware dual-stream network to learn the geometry-aligned representations. Experimental results show that our method achieves good performance on cross-dataset recognition tasks.

中文翻译：

---

为无监督域适应学习自适应几何

摘要 无监督域自适应是解决数据集偏差问题的有效方法。然而，大多数现有的无监督域自适应方法都假设数据分布的几何结构在源域和目标域中是相似的。这个假设在许多实际应用中是无效的，因为训练和测试数据集通常在变异模式和/或变异程度方面存在差异。本文通过对齐数据表示和几何图形来处理几何图形不一致的问题。为了克服对齐几何中缺少目标标签的问题，本文提出学习从域共享标签空间派生的自适应几何。源和目标几何通过使用自适应几何的统一标准进行约束来对齐。结合自适应几何学习和对抗性学习技术，我们开发了一个几何感知双流网络来学习几何对齐的表示。实验结果表明，我们的方法在跨数据集识别任务上取得了良好的性能。