《Unsupervised Domain Adaptation with Random Walks on Target Labelings》閱讀理解
《Unsupervised Domain Adaptation with Random Walks on Target Labelings》這篇文章用隨機漫步的方法來改進無監督域適應算法。 本文的摘要中寫道：「We cast domain adaptation as the problem of finding stable labels for target examples」，文章將域適應的過
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相關文章
- 1. 【論文閱讀】Deep Cocktail Network: Multi-source Unsupervised Domain Adaptation with Category Shift
- 2. 【論文閱讀】Universal Domain Adaptation
- 3. Unsupervised Domain Adaptation with Residual Transfer Networks（2017）
- 4. [cvpr2017]Unsupervised Pixel–Level Domain Adaptation with Generative Adversarial Networks
- 5. Unsupervised domain adaptation with residual transfer networks(NIPS 2016)
- 6. Maximum Classifier Discrepancy for Unsupervised Domain Adaptation
- 7. Unsupervised Domain Adaptation by Backpropagation(2015)
- 8. M-ADDA: Unsupervised Domain Adaptation with Deep Metric Learning
- 9. [論文閱讀筆記@2019.6] Maximum Classifier Discrepancy for Unsupervised Domain Adaptation
- 10. Re-weighted adversarial adaptation network for unsupervised domain adaptation