Keywords: domain adaptation, Bayesian methods, distribution matching, data privacy, class imbalance, computer vision, deep learning
TL;DR: A probabilistic prototype-oriented framework that addresses the issues of sampling variability, class imbalance, and data privacy concerns for unsupervised domain adaptation.
Abstract: Existing methods for unsupervised domain adaptation often rely on minimizing some statistical distance between the source and target samples in the latent space. To avoid the sampling variability, class imbalance, and data-privacy concerns that often plague these methods, we instead provide a memory and computation-efficient probabilistic framework to extract class prototypes and align the target features with them. We demonstrate the general applicability of our method on a wide range of scenarios, including single-source, multi-source, class-imbalance, and source-private domain adaptation. Requiring no additional model parameters and having a moderate increase in computation over the source model alone, the proposed method achieves competitive performance with state-of-the-art methods.
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Supplementary Material: pdf
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