[cvpr2017]Learning an Invariant Hilbert Space for Domain Adaptation
馬氏距離 Introduction 本文適用於半監督和無監督形式的domain adaptation 作者嘗試開發一個幾何解決方案，通過利用黎曼幾何的概念學習潛在空間（latent space）的投影和馬氏距離。 作者建議從source domain和target domain沿着相關聯的映射學習潛在空間（latent space）的結構，以解決無監督和半監督DA的兩個問題。 爲此，作何提出在la
相關文章
- 1. Learning Texture Invariant Representation for Domain Adaptation of Semantic Segmentation
- 2. Domain Adaptation for Object Recognition: An Unsupervised Approach
- 3. [cvpr2017]Joint Geometrical and Statistical Alignment for Visual Domain Adaptation
- 4. Maximum Classifier Discrepancy for Unsupervised Domain Adaptation
- 5. 論文翻譯:Learning Invariant Representations and Risks for Semi-supervised Domain Adaptation
- 6. Domain Adaptation總結（2017.9）
- 7. 2019 AAAI Transferable Attention for Domain Adaptation
- 8. Re-weighted adversarial adaptation network for unsupervised domain adaptation
- 9. CVPR2019 Transferrable Prototypical Networks for Unsupervised Domain Adaptation
- 10. Geodesic flow kernel for unsupervised domain adaptation
- 更多相關文章...
- • XSLT
- • Swift for 循環 - Swift 教程
- • 爲了進字節跳動，我精選了29道Java經典算法題，帶詳細講解
- • RxJava操作符（一）Creating Observables
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. Learning Texture Invariant Representation for Domain Adaptation of Semantic Segmentation
- 2. Domain Adaptation for Object Recognition: An Unsupervised Approach
- 3. [cvpr2017]Joint Geometrical and Statistical Alignment for Visual Domain Adaptation
- 4. Maximum Classifier Discrepancy for Unsupervised Domain Adaptation
- 5. 論文翻譯:Learning Invariant Representations and Risks for Semi-supervised Domain Adaptation
- 6. Domain Adaptation總結（2017.9）
- 7. 2019 AAAI Transferable Attention for Domain Adaptation
- 8. Re-weighted adversarial adaptation network for unsupervised domain adaptation
- 9. CVPR2019 Transferrable Prototypical Networks for Unsupervised Domain Adaptation
- 10. Geodesic flow kernel for unsupervised domain adaptation