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A Prototype-Oriented Framework for Unsupervised Domain Adaptation
Korawat Tanwisuth · Xinjie Fan · Huangjie Zheng · Shujian Zhang · Hao Zhang · Bo Chen · Mingyuan Zhou

Tue Dec 07 04:30 PM -- 06:00 PM (PST) @

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.

Author Information

Korawat Tanwisuth (The University of Texas at Austin)
Xinjie Fan (UT Austin)
Huangjie Zheng (University of Texas, Austin)
Shujian Zhang (UT Austin)
Hao Zhang (Cornell University)
Bo Chen (Xidian University)
Mingyuan Zhou (University of Texas at Austin)

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