Bidirectional Learning for Domain Adaptation of Semantic Segmentation詳讀
Bidirectional Learning for Domain Adaptation of Semantic Segmentation 簡介 具體方法 簡介 語義分割在深度網絡的驅使下發展迅速，但是大規模的數據標註太過昂貴。雖然，近期的工作可以通過生成式網絡使計算機生成帶標註的比較逼真的圖像，但是這種方式生成的圖像存在域不匹配的問題，即計算機生成的圖像（源域）和測試時使用的圖像（目標域）之間的
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相關文章
- 1. Learning Texture Invariant Representation for Domain Adaptation of Semantic Segmentation
- 2. Fully Convolutional Adaptation Networks for Semantic Segmentation
- 3. Learning from Synthetic Data: Addressing Domain Shift for Semantic Segmentation
- 4. Unsupervised Intra-domain Adaptation for Semantic Segmentation through Self-Supervision
- 5. 論文閱讀 | A Curriculum Domain Adaptation Approach to the Semantic Segmentation of Urban Scenes
- 6. 論文閱讀：Unsupervised Intra-domain Adaptation for Semantic Segmentation through Self-Supervision
- 7. 讀論文《SSF-DAN: Separated Semantic Feature based Domain Adaptation Network for Semantic Segmentation》記錄
- 8. Review of Semantic Segmentation with Deep Learning
- 9. 《Unsupervised Intra-domain Adaptation for Semantic Segmentation through Self-Supervision》論文筆記
- 10. Maximum Classifier Discrepancy for Unsupervised Domain Adaptation