In this article, the unsupervised domain adaptation problem, where an approximate inference model is to be learned from a labeled dataset and expected to generalize well on an unlabeled dataset, is considered. Unlike the existing work, we explicitly unveil the importance of the latent variables produced by the feature extractor, that is, encoder, where contains the most representative information about their input samples, for the knowledge transfer. We argue that an estimator of the representation of the two datasets can be used as an agent for knowledge transfer. To be specific, a novel variational inference approach is proposed to approximate a latent distribution from the unlabeled dataset that can be used to accurately predict its input samples. It is demonstrated that the discriminative knowledge of the latent distribution that is learned from the labeled dataset can be progressively transferred to that is learned from the unlabeled dataset by simultaneously optimizing the estimator via the variational inference and our proposed regularization for shifting the mean of the estimator. The experiments on several benchmark datasets demonstrate that the proposed method consistently outperforms state-of-the-art methods for both object classification and digit classification.

中文翻译：

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互变分推理：无监督域适应的间接变分推理方法


在本文中，考虑了无监督域适应问题，其中从标记数据集学习近似推理模型，并期望在未标记数据集上很好地泛化。与现有的工作不同，我们明确揭示了特征提取器（即编码器）产生的潜在变量对于知识转移的重要性，其中包含有关其输入样本的最具代表性的信息。我们认为两个数据集表示的估计器可以用作知识转移的代理。具体来说，提出了一种新颖的变分推理方法来近似未标记数据集的潜在分布，该数据集可用于准确预测其输入样本。结果表明，通过变分推理和我们提出的用于移动估计量均值的正则化同时优化估计器，从标记数据集中学习到的潜在分布的判别知识可以逐步转移到从未标记数据集中学习到的潜在分布知识。在几个基准数据集上的实验表明，所提出的方法在对象分类和数字分类方面始终优于最先进的方法。