[cvpr2017]Learning an Invariant Hilbert Space for Domain Adaptation
马氏距离 Introduction 本文适用于半监督和无监督形式的domain adaptation 作者尝试开发一个几何解决方案，通过利用黎曼几何的概念学习潜在空间（latent space）的投影和马氏距离。 作者建议从source domain和target domain沿着相关联的映射学习潜在空间（latent space）的结构，以解决无监督和半监督DA的两个问题。 为此，作何提出在la
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相关文章
- 1. Learning Texture Invariant Representation for Domain Adaptation of Semantic Segmentation
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