2021
DOI: 10.48550/arxiv.2107.13467
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Recursively Conditional Gaussian for Ordinal Unsupervised Domain Adaptation

Abstract: 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. … Show more

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