域適應方法系列1：Asymmetric Tri-training for Unsupervised Domain Adaptation
1、Asymmetric Tri-training for Unsupervised Domain Adaptation–非對抗方法 研究域適應，都希望能夠學到域不變的特徵，也就是說，假如現在是分類的任務，希望學習到分類器在不同的域都能夠表現良好。實際情況是，這是很難的事情，因爲不同域的具有不同的分佈，具有各自具體域的特性。關於這方面的解決方法很多，看下本文Asymmetric T
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相關文章
- 1. Geodesic flow kernel for unsupervised domain adaptation
- 2. Maximum Classifier Discrepancy for Unsupervised Domain Adaptation
- 3. 域適應系列3：Minimal-entropy Correlation Alignment for Unsupervised Deep Domain Adaptation（ICLR 2018）
- 4. 論文筆記 —— 《Asymmetric Tri-training for Unsupervised Domain Adaptation》
- 5. 【論文筆記】Asymmetric Tri-training for Unsupervised Domain Adaptation
- 6. Re-weighted adversarial adaptation network for unsupervised domain adaptation
- 7. Domain Adaptation for Object Recognition: An Unsupervised Approach
- 8. CVPR2019 Transferrable Prototypical Networks for Unsupervised Domain Adaptation
- 9. 域適應學習筆記：visual Domain Adaptation
- 10. Unsupervised Intra-domain Adaptation for Semantic Segmentation through Self-Supervision