Connect, Not Collapse: Explaining Contrastive Learning for Unsupervised Domain Adaptation

  title={Connect, Not Collapse: Explaining Contrastive Learning for Unsupervised Domain Adaptation},
  author={Kendrick Shen and Robbie Jones and Ananya Kumar and Sang Michael Xie and Jeff Z. HaoChen and Tengyu Ma and Percy Liang},
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… 
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  • Computer Science
    2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
  • 2021
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