论文标题
FIXBI：无监督域适应的桥接域空间
FixBi: Bridging Domain Spaces for Unsupervised Domain Adaptation
论文作者
论文摘要
无监督的域适应性（UDA）方法用于学习域不变表示已取得了显着的进步。但是，大多数研究都是基于从源域对目标域的直接适应，并且遭受了较大的域差异。在本文中，我们提出了一种有效处理如此大的领域差异的UDA方法。我们引入了基于固定比率的混合，以增加源和目标域之间的多个中间域。从增强域中，我们训练具有互补特征的源含量模型和目标赋予模型。使用我们基于置信的学习方法，例如，使用低信任预测，与高信心预测和自我培养的双向匹配，模型可以互相学习或从其自身的结果中学习。通过我们提出的方法，模型逐渐将域知识从源转移到目标域。广泛的实验证明了我们提出的方法对三个公共基准测试的优越性：Office-31，Office Home和Visda-2017。
Unsupervised domain adaptation (UDA) methods for learning domain invariant representations have achieved remarkable progress. However, most of the studies were based on direct adaptation from the source domain to the target domain and have suffered from large domain discrepancies. In this paper, we propose a UDA method that effectively handles such large domain discrepancies. We introduce a fixed ratio-based mixup to augment multiple intermediate domains between the source and target domain. From the augmented-domains, we train the source-dominant model and the target-dominant model that have complementary characteristics. Using our confidence-based learning methodologies, e.g., bidirectional matching with high-confidence predictions and self-penalization using low-confidence predictions, the models can learn from each other or from its own results. Through our proposed methods, the models gradually transfer domain knowledge from the source to the target domain. Extensive experiments demonstrate the superiority of our proposed method on three public benchmarks: Office-31, Office-Home, and VisDA-2017.
