We propose a new technique called CHATTY: Coupled Holistic Adversarial Transport Terms with Yield for Unsupervised Domain Adaptation. Adversarial training is commonly used for learning domain-invariant representations by reversing the gradients from a domain discriminator head to train the feature extractor layers of a neural network. We propose significant modifications to the adversarial head, its training objective, and the classifier head. With the aim of reducing class confusion, we introduce a sub-network which displaces the classifier outputs of the source and target domain samples in a learnable manner. We control this movement using a novel transport loss that spreads class clusters away from each other and makes it easier for the classifier to find the decision boundaries for both the source and target domains. The results of adding this new loss to a careful selection of previously proposed losses leads to improvement in UDA results compared to the previous state-of-the-art methods on benchmark datasets. We show the importance of the proposed loss term using ablation studies and visualization of the movement of target domain sample in representation space.

翻译：我们提出一种名为CHATTY（Coupled Holistic Adversarial Transport Terms with Yield）的新技术，用于无监督域适应。对抗训练通常通过反转域判别器头的梯度来训练神经网络的特征提取层，以学习域不变表征。我们对对抗头、其训练目标以及分类器头进行了重大改进。为减少类别混淆，我们引入一个子网络，以可学习方式迁移源域和目标域样本的分类器输出。我们通过一种新型传输损失控制这一迁移，该损失将各类簇相互分散，使分类器更易找到源域和目标域的决策边界。将这一新损失与精心选取的已有损失相结合，在基准数据集上的无监督域适应结果相较于先前最先进方法有所提升。通过消融实验及目标域样本在表征空间中迁移的可视化，我们证明了所提损失项的重要性。