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A Prototype-Oriented Framework for Unsupervised Domain Adaptation
[article]
2021
arXiv
pre-print
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
arXiv:2110.12024v1
fatcat:axhkrc3xijdstg3l7lwsezsy7e