CVPR 2019之迁移学习解读（三）Contrastive Adaptation Network for Unsupervised Domain Adaptation
Contrastive Adaptation Network for Unsupervised Domain Adaptation 简述： 无监督域自适应(UDA)对目标域数据进行预处理，而手工注释只在源域可用。以往的方法在忽略类信息的情况下，会使域间的差异最小化，从而导致不一致和泛化性能低下。目前，虽然有ImageNet这种庞大的拥有精确标记的数据集，但是对于每个特定的目标域或任务，手工标签往往
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相关文章
- 1. Re-weighted adversarial adaptation network for unsupervised domain adaptation
- 2. 迁移学习之Domain Adaptation
- 3. [cvpr2017]Deep Hashing Network for Unsupervised Domain Adaptation
- 4. [CVPR 2018]Maximum Classifier Discrepancy for Unsupervised Domain Adaptation
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- 6. 迁移学习——Domain Adaptation
- 7. Maximum Classifier Discrepancy for Unsupervised Domain Adaptation
- 8. (CVPR 2019)Universal Domain adaptation笔记
- 9. 2019 AAAI Transferable Attention for Domain Adaptation
- 10. CVPR2019 Transferrable Prototypical Networks for Unsupervised Domain Adaptation