论文标题
通过在无监督的域适应中添加额外的类来扩大判别能力
Enlarging Discriminative Power by Adding an Extra Class in Unsupervised Domain Adaptation
论文作者
论文摘要
在本文中，我们研究了无监督的域适应性问题，旨在使用来自源域中的标记数据和来自目标域的未标记数据获得目标域的预测模型。基于提取特征的想法，这些研究不仅是两个领域不变的，而且为目标域提供了很高的歧视能力。在本文中，我们提出了一种授权歧视性的想法：添加一个新的人工阶级，并在数据上训练模型以及新类别的GAN生成的样本。基于新类样本的训练模型能够通过重新定位目标域中的当前类的数据来提取更具歧视性的功能，从而更有效地绘制决策范围。我们的想法是高度通用的，因此它与Dann，Vada和Dirt-T等许多现有方法兼容。我们对通常用于评估无监督域适应的标准数据进行了各种实验，并证明我们的算法在许多情况下都达到了SOTA性能。
In this paper, we study the problem of unsupervised domain adaptation that aims at obtaining a prediction model for the target domain using labeled data from the source domain and unlabeled data from the target domain. There exists an array of recent research based on the idea of extracting features that are not only invariant for both domains but also provide high discriminative power for the target domain. In this paper, we propose an idea of empowering the discriminativeness: Adding a new, artificial class and training the model on the data together with the GAN-generated samples of the new class. The trained model based on the new class samples is capable of extracting the features that are more discriminative by repositioning data of current classes in the target domain and therefore drawing the decision boundaries more effectively. Our idea is highly generic so that it is compatible with many existing methods such as DANN, VADA, and DIRT-T. We conduct various experiments for the standard data commonly used for the evaluation of unsupervised domain adaptations and demonstrate that our algorithm achieves the SOTA performance for many scenarios.
