Domain adversarial training has shown its effective capability for finding domain invariant feature representations and been successfully adopted for various domain adaptation tasks. However, recent advances of large models (e.g., vision transformers) and emerging of complex adaptation scenarios (e.g., DomainNet) make adversarial training being easily biased towards source domain and hardly adapted to target domain. The reason is twofold: relying on large amount of labelled data from source domain for large model training and lacking of labelled data from target domain for fine-tuning. Existing approaches widely focused on either enhancing discriminator or improving the training stability for the backbone networks. Due to unbalanced competition between the feature extractor and the discriminator during the adversarial training, existing solutions fail to function well on complex datasets. To address this issue, we proposed a novel contrastive adversarial training (CAT) approach that leverages the labeled source domain samples to reinforce and regulate the feature generation for target domain. Typically, the regulation forces the target feature distribution being similar to the source feature distribution. CAT addressed three major challenges in adversarial learning: 1) ensure the feature distributions from two domains as indistinguishable as possible for the discriminator, resulting in a more robust domain-invariant feature generation; 2) encourage target samples moving closer to the source in the feature space, reducing the requirement for generalizing classifier trained on the labeled source domain to unlabeled target domain; 3) avoid directly aligning unpaired source and target samples within mini-batch. CAT can be easily plugged into existing models and exhibits significant performance improvements.

翻译：领域对抗训练在寻找领域不变特征表示方面展现出显著效果，并已成功应用于多种领域自适应任务。然而，近期大模型（如视觉Transformer）的进展与复杂适应场景（如DomainNet）的出现，使得对抗训练容易偏向源领域且难以适应目标领域。其原因有二：依赖大量源领域标注数据进行大模型训练，以及缺乏目标领域标注数据进行微调。现有方法多集中于增强判别器或提升主干网络的训练稳定性。由于对抗训练中特征提取器与判别器之间的竞争失衡，现有解决方案在复杂数据集上难以有效发挥作用。为解决此问题，我们提出了一种新颖的对比式对抗训练方法，该方法利用已标注的源领域样本来增强和调控目标领域的特征生成。具体而言，这种调控机制强制目标特征分布与源特征分布趋近。CAT解决了对抗学习中的三个主要挑战：1）确保两个领域的特征分布在判别器视角下尽可能不可区分，从而生成更具鲁棒性的领域不变特征；2）促使目标样本在特征空间中向源样本靠拢，降低将基于标注源领域训练的分类器泛化至未标注目标领域的要求；3）避免在小批量内直接对齐未配对的源样本与目标样本。CAT可轻松集成到现有模型中，并展现出显著的性能提升。