[cvpr2017]Unsupervised Pixel–Level Domain Adaptation with Generative Adversarial Networks
優勢 與Task-Specific圖像訓練結構解耦合。 作者說因爲這個域適應是在像素級別上進行的（ because our model maps one image to another at the pixel level），所以我們可以改變這個Task-Specific圖像訓練結構 在標籤域也可以同樣做到域適應 target domain和source domain的標籤域可以不一樣，targ
相關文章
- 1. 遷移學習-PixelDA-Unsupervised Pixel–Level Domain Adaptation with Generative Adversarial Networks
- 2. Domain Adaptation 與 Generative Adversarial Network
- 3. [cvpr2017]Adversarial Discriminative Domain Adaptation
- 4. 論文筆記：Conditional Coupled Generative Adversarial Networks for Zero-Shot Domain Adaptation
- 5. Multi-Adversarial Domain Adaptation
- 6. FACE AGING WITH CONDITIONAL GENERATIVE ADVERSARIAL NETWORKS
- 7. Domain Adaptation總結（2017.9）
- 8. ADDA: Adversarial Discriminative Domain Adaptation
- 9. Image-Image Domain Adaptation with Preserved Self-Similarity andDomain-Dissimilarity for Person Re-i
- 10. Graphical Generative Adversarial Networks
- 更多相關文章...
- • XSLT
- • W3C DOM Activities - W3C 教程
- • 算法總結-廣度優先算法
- • 爲了進字節跳動，我精選了29道Java經典算法題，帶詳細講解
相關標籤/搜索
每日一句
-
每一个你不满意的现在，都有一个你没有努力的曾经。
最新文章
歡迎關注本站公眾號,獲取更多信息
相關文章
- 1. 遷移學習-PixelDA-Unsupervised Pixel–Level Domain Adaptation with Generative Adversarial Networks
- 2. Domain Adaptation 與 Generative Adversarial Network
- 3. [cvpr2017]Adversarial Discriminative Domain Adaptation
- 4. 論文筆記：Conditional Coupled Generative Adversarial Networks for Zero-Shot Domain Adaptation
- 5. Multi-Adversarial Domain Adaptation
- 6. FACE AGING WITH CONDITIONAL GENERATIVE ADVERSARIAL NETWORKS
- 7. Domain Adaptation總結（2017.9）
- 8. ADDA: Adversarial Discriminative Domain Adaptation
- 9. Image-Image Domain Adaptation with Preserved Self-Similarity andDomain-Dissimilarity for Person Re-i
- 10. Graphical Generative Adversarial Networks