Learning to Adapt Structured Output Space for Semantic Segmentation——CVPR2018
Learning to Adapt Structured Output Space for Semantic Segmentation——CVPR2018 前面【1】一篇，有提到用对抗loss和半监督来做分割。而这篇可以说是对抗loss和域适配结合起来做的另一个工作了。 背景： 知识迁移和域适配：目标域(target domains，没有标注)和源域(source domains，有标注)是具有相
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相关文章
- 1. Learning to Adapt Structured Output Space for Semantic Segmentation
- 2. 《AdaptSegNet：Learning to Adapt Structured Output Space for Semantic Segmentation》论文笔记
- 3. 论文域适应语义分割：Learning to Adapt Structured Output Space for Semantic Segmentation
- 4. Context Encoding for Semantic Segmentation[CVPR2018]
- 5. Structured Knowledge Distillation for Semantic Segmentation
- 6. 【论文阅读】Structured Knowledge Distillation for Semantic Segmentation
- 7. 《Learning Dynamic Routing for Semantic Segmentation》
- 8. Learning from Synthetic Data: Addressing Domain Shift for Semantic Segmentation
- 9. 【论文笔记】Learning Deconvolution Network for Semantic Segmentation
- 10. Learning Deconvolution Network for Semantic Segmentation