We consider unsupervised domain adaptation (UDA), where labeled data from a
source domain (e.g., photographs) and unlabeled data from a target domain
(e.g., sketches) are used to learn a classifier for the target domain.
Conventional UDA methods (e.g., domain adversarial training) learn
domain-invariant features to improve generalization to the target domain. In
this paper, we show that contrastive pre-training, which learns features on
unlabeled source and target data and then fine-tunes on labeled source data, is
competitive with strong UDA methods. However, we find that contrastive
pre-training does not learn domain-invariant features, diverging from
conventional UDA intuitions. We show theoretically that contrastive
pre-training can learn features that vary subtantially across domains but still
generalize to the target domain, by disentangling domain and class information.
Our results suggest that domain invariance is not necessary for UDA. We
empirically validate our theory on benchmark vision datasets.

本文针对无监督域适应问题，提出了一种基于对比度预训练的分类器学习方法，该方法可以学习到跨领域的分类特征而无需在领域间建立不变特征映射，并在基准视觉数据集上获得了验证。