There has been a growing interest in unsupervised domain adaptation (UDA) to alleviate the data scalability issue, while the existing works usually focus on classifying independently discrete labels. However, in many tasks (e.g., medical diagnosis), the labels are discrete and successively distributed. The UDA for ordinal classification requires inducing non-trivial ordinal distribution prior to the latent space. Target for this, the partially ordered set (poset) is defined for constraining the latent vector. Instead of the typically i.i.d. Gaussian latent prior, in this work, a recursively conditional Gaussian (RCG) set is proposed for ordered constraint modeling, which admits a tractable joint distribution prior. Furthermore, we are able to control the density of content vectors that violate the poset constraint by a simple "three-sigma rule". We explicitly disentangle the cross-domain images into a shared ordinal prior induced ordinal content space and two separate source/target ordinal-unrelated spaces, and the self-training is worked on the shared space exclusively for ordinal-aware domain alignment. Extensive experiments on UDA medical diagnoses and facial age estimation demonstrate its effectiveness.

中文翻译：

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用于有序无监督域适应的递归条件高斯

人们对无监督域适应 (UDA) 越来越感兴趣，以缓解数据可扩展性问题，而现有工作通常侧重于对独立离散标签进行分类。然而，在许多任务（例如，医学诊断）中，标签是离散的并且是连续分布的。用于序数分类的 UDA 需要在潜在空间之前引入非平凡的序数分布。为此，定义了偏序集（poset）来约束潜在向量。在这项工作中，不是典型的 iid 高斯潜在先验，而是提出了递归条件高斯 (RCG) 集用于有序约束建模，它允许易处理的联合分布先验。此外，我们能够通过一个简单的“