Learning Transferable Features with Deep Adaptation Networks
經典文章DAN 總函數： 上面的公式中，J函數是一組有標籤樣本的損失，dk2是第l層的mk-mmd距離。 總函數調整的參數是θ，應該是1-8層（1-3層是固定的，4-5是fine-tune，6-8層是learn） ？？？fine-tune、learn的區別 kernel parameter β是怎麼學習的？ 本文的創新點： （參考：對於DAN方法的解讀-Learni
相關文章
- 1. Learning Transferable Features with Deep Adaptation Networks
- 2. 翻譯「Learning Transferable Features with Deep Adaptation Networks」
- 3. 對於DAN方法的解讀-Learning Transferable Features with Deep Adaptation Networks
- 4. How transferable are features in deep nerual networks? 【paper review】
- 5. How transferable are features in deep neural networks?
- 6. Dense biased networks with deep priori anatomy and hard region adaptation: Semi-supervised learning
- 7. M-ADDA: Unsupervised Domain Adaptation with Deep Metric Learning
- 8. How transferable are features in deep neural networks? 論文筆記
- 9. 論文筆記：How transferable are features in deep neural networks?
- 10. Unsupervised Domain Adaptation with Residual Transfer Networks（2017）
- 更多相關文章...
- • XSLT
- • PHP curl_version函數 - PHP參考手冊
- • Java Agent入門實戰（一）-Instrumentation介紹與使用
- • Java Agent入門實戰（三）-JVM Attach原理與使用
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。
歡迎關注本站公眾號,獲取更多信息
相關文章
- 1. Learning Transferable Features with Deep Adaptation Networks
- 2. 翻譯「Learning Transferable Features with Deep Adaptation Networks」
- 3. 對於DAN方法的解讀-Learning Transferable Features with Deep Adaptation Networks
- 4. How transferable are features in deep nerual networks? 【paper review】
- 5. How transferable are features in deep neural networks?
- 6. Dense biased networks with deep priori anatomy and hard region adaptation: Semi-supervised learning
- 7. M-ADDA: Unsupervised Domain Adaptation with Deep Metric Learning
- 8. How transferable are features in deep neural networks? 論文筆記
- 9. 論文筆記：How transferable are features in deep neural networks?
- 10. Unsupervised Domain Adaptation with Residual Transfer Networks（2017）