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The unsupervised domain adaptation (UDA) has been widely adopted to alleviatethe data scalability issue, while the existing works usually focus onclassifying independently discrete labels. However, in many tasks (e.g.,medical diagnosis), the labels are discrete and successively distributed. TheUDA for ordinal classification requires inducing non-trivial ordinaldistribution prior to the latent space. Target for this, the partially orderedset (poset) is defined for constraining the latent vector. Instead of thetypically i.i.d. Gaussian latent prior, in this work, a recursively conditionalGaussian (RCG) set is adapted for ordered constraint modeling, which admits atractable joint distribution prior. Furthermore, we are able to control thedensity of content vector that violates the poset constraints by a simple"three-sigma rule". We explicitly disentangle the cross-domain images into ashared ordinal prior induced ordinal content space and two separatesource/target ordinal-unrelated spaces, and the self-training is worked on theshared space exclusively for ordinal-aware domain alignment. Extensiveexperiments on UDA medical diagnoses and facial age estimation demonstrate itseffectiveness.

Recursively Conditional Gaussian for Ordinal Unsupervised Domain Adaptation