Recursively Conditional Gaussian for Ordinal Unsupervised Domain Adaptation

Xiaofeng Liu, Site Li, Yubin Ge, Pengyi Ye, Jia You, Jun Lu

Research output: Chapter in book / Conference proceedingConference article published in proceeding or bookAcademic researchpeer-review


The unsupervised domain adaptation (UDA) has been widely adopted 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 adapted for ordered constraint modeling, which admits a tractable joint distribution prior Furthermore, we are able to control the density of content vector that violates the poset constraints 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.
Original languageEnglish
Title of host publicationProc. of ICCV'2021
Publication statusPublished - Oct 2021
Event2021 IEEE/CVF Int. Conf. on Computer Vision - Montreal, QC, Canada
Duration: 10 Oct 202117 Oct 2021


Conference2021 IEEE/CVF Int. Conf. on Computer Vision
Abbreviated titleICCV


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