Re-weighted adversarial adaptation network for unsupervised domain adaptation
摘要主要強調以下幾點： 1. 當domain discrepancy比較大的時候，文章中方法的性能比其餘方法要好的多（實驗部分的確如此） 2. re-weighted：在訓練discriminator D的時候，每一個source sample並不是同等重要，weight取決於label 3. 在匹配source sample和target sample的特徵分佈的時候，用的是基於EM 距離的OT
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相關文章
- 1. Domain Adaptation 與 Generative Adversarial Network
- 2. Maximum Classifier Discrepancy for Unsupervised Domain Adaptation
- 3. [cvpr2017]Deep Hashing Network for Unsupervised Domain Adaptation
- 4. Multi-Adversarial Domain Adaptation
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- 6. [cvpr2017]Unsupervised Pixel–Level Domain Adaptation with Generative Adversarial Networks
- 7. ADDA: Adversarial Discriminative Domain Adaptation
- 8. [cvpr2017]Adversarial Discriminative Domain Adaptation
- 9. Unsupervised Domain Adaptation by Backpropagation(2015)
- 10. 2019 AAAI Transferable Attention for Domain Adaptation