（CVPR2020）Model Adaptation: Unsupervised Domain Adaptation without Source Data總結
Model Adaptation: Unsupervised Domain Adaptation without Source Data 本文提出一個新的問題，沒有源域數據的無監督領域自適應問題。 說是沒有源域數據，其實指的是在Domain Adaptation的過程中不使用源域數據。以本文爲例，它仍然使用了使用源域數據預訓練出的分類模型。 模型結構 本文主要結構包括三個部分，生成器 G G G，
相關文章
- 1. Domain Adaptation without source data閱讀筆記
- 2. Domain Adaptation總結（2017.9）
- 3. Re-weighted adversarial adaptation network for unsupervised domain adaptation
- 4. Maximum Classifier Discrepancy for Unsupervised Domain Adaptation
- 5. Unsupervised Domain Adaptation by Backpropagation(2015)學習總結
- 6. Multi-source Distilling Domain Adaptation
- 7. Unsupervised Domain Adaptation by Backpropagation(2015)
- 8. [MICCAI2019]Unsupervised Domain Adaptation via Disentangled Representations
- 9. Geodesic flow kernel for unsupervised domain adaptation
- 10. [cvpr2017]Unsupervised Pixel–Level Domain Adaptation with Generative Adversarial Networks
- 更多相關文章...
- • RSS
元素 - RSS 教程 - • Docker 資源彙總 - Docker教程
- • 算法總結-雙指針
- • 算法總結-回溯法
相關標籤/搜索
每日一句
-
每一个你不满意的现在，都有一个你没有努力的曾经。
歡迎關注本站公眾號,獲取更多信息
相關文章
- 1. Domain Adaptation without source data閱讀筆記
- 2. Domain Adaptation總結（2017.9）
- 3. Re-weighted adversarial adaptation network for unsupervised domain adaptation
- 4. Maximum Classifier Discrepancy for Unsupervised Domain Adaptation
- 5. Unsupervised Domain Adaptation by Backpropagation(2015)學習總結
- 6. Multi-source Distilling Domain Adaptation
- 7. Unsupervised Domain Adaptation by Backpropagation(2015)
- 8. [MICCAI2019]Unsupervised Domain Adaptation via Disentangled Representations
- 9. Geodesic flow kernel for unsupervised domain adaptation
- 10. [cvpr2017]Unsupervised Pixel–Level Domain Adaptation with Generative Adversarial Networks