[cvpr2017]Unsupervised Pixel–Level Domain Adaptation with Generative Adversarial Networks
优势 与Task-Specific图像训练结构解耦合。 作者说因为这个域适应是在像素级别上进行的（ because our model maps one image to another at the pixel level），所以我们可以改变这个Task-Specific图像训练结构 在标签域也可以同样做到域适应 target domain和source domain的标签域可以不一样，targ
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相关文章
- 1. 迁移学习-PixelDA-Unsupervised Pixel–Level Domain Adaptation with Generative Adversarial Networks
- 2. Domain Adaptation 与 Generative Adversarial Network
- 3. [cvpr2017]Adversarial Discriminative Domain Adaptation
- 4. 论文笔记：Conditional Coupled Generative Adversarial Networks for Zero-Shot Domain Adaptation
- 5. Multi-Adversarial Domain Adaptation
- 6. FACE AGING WITH CONDITIONAL GENERATIVE ADVERSARIAL NETWORKS
- 7. Domain Adaptation总结（2017.9）
- 8. ADDA: Adversarial Discriminative Domain Adaptation
- 9. Image-Image Domain Adaptation with Preserved Self-Similarity andDomain-Dissimilarity for Person Re-i
- 10. Graphical Generative Adversarial Networks