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讀論文《SSF-DAN: Separated Semantic Feature based Domain Adaptation Network for Semantic Segmentation》記錄
時間 2020-12-30
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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
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6.
《Unsupervised Intra-domain Adaptation for Semantic Segmentation through Self-Supervision》論文筆記
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【論文學習記錄】Learning a Discriminative Feature Network for Semantic Segmentation
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【論文閱讀】Learning Deconvolution Network for Semantic Segmentation
9.
Learning Texture Invariant Representation for Domain Adaptation of Semantic Segmentation
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【論文筆記】Learning Deconvolution Network for Semantic Segmentation
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