Unsupervised Domain Adaptation by Backpropagation(2015)
Introduction 論文作者希望網絡學習由discriminativeness（分歧，應該是source domain和target domain的區別吧）和domain-invariance組合起來的隱藏特徵（underlying feature） 通過同時優化以下兩個discriminative classifiers來實現 label predictor預測分類標籤（訓練與測試的時候）
相關文章
- 1. 論文筆記：Unsupervised Domain Adaptation by Backpropagation
- 2. 論文筆記 ——《Unsupervised Domain Adaptation by Backpropagation》
- 3. Unsupervised Domain Adaptation by Backpropagation(2015)學習總結
- 4. Unsupervised Domain Adaptation by Backpropagation(2015)學習筆記
- 5. Maximum Classifier Discrepancy for Unsupervised Domain Adaptation
- 6. Geodesic flow kernel for unsupervised domain adaptation
- 7. [MICCAI2019]Unsupervised Domain Adaptation via Disentangled Representations
- 8. Re-weighted adversarial adaptation network for unsupervised domain adaptation
- 9. DANN：Unsupervised Domain Adaptation by Backpropagation
- 10. Unsupervised domain adaptation with residual transfer networks(NIPS 2016)
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相關文章
- 1. 論文筆記：Unsupervised Domain Adaptation by Backpropagation
- 2. 論文筆記 ——《Unsupervised Domain Adaptation by Backpropagation》
- 3. Unsupervised Domain Adaptation by Backpropagation(2015)學習總結
- 4. Unsupervised Domain Adaptation by Backpropagation(2015)學習筆記
- 5. Maximum Classifier Discrepancy for Unsupervised Domain Adaptation
- 6. Geodesic flow kernel for unsupervised domain adaptation
- 7. [MICCAI2019]Unsupervised Domain Adaptation via Disentangled Representations
- 8. Re-weighted adversarial adaptation network for unsupervised domain adaptation
- 9. DANN：Unsupervised Domain Adaptation by Backpropagation
- 10. Unsupervised domain adaptation with residual transfer networks(NIPS 2016)