用挑選代表的技術來改進非監督領域自適應

Abstract

領域自適應是一種解決數據集分佈改變的技術，其中訓練 (源域) 資料和測試 (目標域) 資料可能來自不同的分佈。目前的研究主要集中在共變量分佈改變和標籤分佈改變這兩種設置，而不同的設置下，對源域和目標域之間的關聯會做出不同的假設。然而，我們觀察到這兩種設置都不能完全滿足現實世界生物化學的應用需求。我們仔細研究了這些設置在應用層面遇到的困難，並提出了一種新穎的解決方法，它將兩種設置都考慮在內以提高應用上的性能表現。我們提出的解決方法的關鍵想法是從源域數據中挑選與目標域分佈相似的數據。我們更進一步探索兩種挑選的方案，將相似性嵌入最近鄰居法風格的硬選擇方案，以及透過軟性約束來強制相似性的軟選擇方案。實驗顯示我們提出的解決方案不僅可以達到更高的精準度在生物化學應用上，而且在能具體定義相似性的時候，其他領域自適應的任務上也展示出有希望的性能表現。

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.