Stein Discrepancy for Unsupervised Domain Adaptation
This work addresses domain adaptation for machine learning applications where labeled target data is limited, representing an incremental improvement with a novel method for a known bottleneck.
The paper tackles the problem of unsupervised domain adaptation (UDA) when target data is scarce by proposing a method based on Stein discrepancy to measure domain distance, resulting in improved accuracy over existing methods in such scenarios.
Unsupervised domain adaptation (UDA) leverages information from a labeled source dataset to improve accuracy on a related but unlabeled target dataset. A common approach to UDA is aligning representations from the source and target domains by minimizing the distance between their data distributions. Previous methods have employed distances such as Wasserstein distance and maximum mean discrepancy. However, these approaches are less effective when the target data is significantly scarcer than the source data. Stein discrepancy is an asymmetric distance between distributions that relies on one distribution only through its score function. In this paper, we propose a novel UDA method that uses Stein discrepancy to measure the distance between source and target domains. We develop a learning framework using both non-kernelized and kernelized Stein discrepancy. Theoretically, we derive an upper bound for the generalization error. Numerical experiments show that our method outperforms existing methods using other domain discrepancy measures when only small amounts of target data are available.