Improving Unsupervised Domain Adaptation with Representative Selection Techniques

Domain adaptation is a technique that tackles the dataset shift scenario, where the training (source) data and the test (target) data can come from different distributions. Current research works mainly focus on either the covariate shift or the label shift settings, each making a different assumption on how the source and target data are related. Nevertheless, we observe that neither of the settings can perfectly match the needs of a real-world bio-chemistry application. We carefully study the difficulties encountered by those settings on the application and propose a novel method that takes both settings into account to improve the performance on the application. The key idea of our proposed method is to select examples from the source data that are similar to the target distribution of interest. We further explore two selection schemes, the hard-selection scheme that plugs similarity into a nearest-neighbor style approach, and the soft-selection scheme that enforces similarity by soft constraints. Experiments demonstrate that our proposed method not only achieves better accuracy for the bio-chemistry application but also shows promising performance on other domain adaptation tasks when the similarity can be concretely defined.