读论文《SSF-DAN: Separated Semantic Feature based Domain Adaptation Network for Semantic Segmentation》记录
前言： 本篇论文是在《No More Discrimination: Cross City Adaptation of Road Scene Segmenters》的基础上发展起来的。 semantic-wise 传统的class-wise域自适应方法，使用全部的伪标签来作为对抗学习分类的标签，但是由于伪标签不一定全部正确，所以此方法存在一些问题–每个class-wise之间的域自适应方向可能不一
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相关文章
- 1. Bidirectional Learning for Domain Adaptation of Semantic Segmentation详读
- 2. Unsupervised Intra-domain Adaptation for Semantic Segmentation through Self-Supervision
- 3. Fully Convolutional Adaptation Networks for Semantic Segmentation
- 4. Semantic Segmentation---DFANet:Deep Feature Aggregation for Real-Time Semantic Segmentation（论文解读十）
- 5. 论文阅读：Unsupervised Intra-domain Adaptation for Semantic Segmentation through Self-Supervision
- 6. 《Unsupervised Intra-domain Adaptation for Semantic Segmentation through Self-Supervision》论文笔记
- 7. 【论文学习记录】Learning a Discriminative Feature Network for Semantic Segmentation
- 8. 【论文阅读】Learning Deconvolution Network for Semantic Segmentation
- 9. Learning Texture Invariant Representation for Domain Adaptation of Semantic Segmentation
- 10. 【论文笔记】Learning Deconvolution Network for Semantic Segmentation