讀論文《SSF-DAN: Separated Semantic Feature based Domain Adaptation Network for Semantic Segmentation》記錄
前言： 本篇論文是在《No More Discrimination: Cross City Adaptation of Road Scene Segmenters》的基礎上發展起來的。 semantic-wise 傳統的class-wise域自適應方法，使用全部的僞標籤來作爲對抗學習分類的標籤，但是由於僞標籤不一定全部正確，所以此方法存在一些問題–每個class-wise之間的域自適應方向可能不一
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相關文章
- 1. Bidirectional Learning for Domain Adaptation of Semantic Segmentation詳讀
- 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（論文解讀十）
- 5. 論文閱讀：Unsupervised Intra-domain Adaptation for Semantic Segmentation through Self-Supervision
- 6. 《Unsupervised Intra-domain Adaptation for Semantic Segmentation through Self-Supervision》論文筆記
- 7. 【論文學習記錄】Learning a Discriminative Feature Network for Semantic Segmentation
- 8. 【論文閱讀】Learning Deconvolution Network for Semantic Segmentation
- 9. Learning Texture Invariant Representation for Domain Adaptation of Semantic Segmentation
- 10. 【論文筆記】Learning Deconvolution Network for Semantic Segmentation