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，有提到用對抗loss和半監督來做分割。而這篇可以說是對抗loss和域適配結合起來做的另一個工作了。 背景： 知識遷移和域適配：目標域(target domains，沒有標註)和源域(source domains，有標註)是具有相
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相關文章
- 1. Learning to Adapt Structured Output Space for Semantic Segmentation
- 2. 《AdaptSegNet：Learning to Adapt Structured Output Space for Semantic Segmentation》論文筆記
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- 4. Context Encoding for Semantic Segmentation[CVPR2018]
- 5. Structured Knowledge Distillation for Semantic Segmentation
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- 7. 《Learning Dynamic Routing for Semantic Segmentation》
- 8. Learning from Synthetic Data: Addressing Domain Shift for Semantic Segmentation
- 9. 【論文筆記】Learning Deconvolution Network for Semantic Segmentation
- 10. Learning Deconvolution Network for Semantic Segmentation