對於DAN方法的解讀-Learning Transferable Features with Deep Adaptation Networks
上週彙報了該篇經典文章，現在回顧並且記錄一下自己對DAN方法的理解 深度適配網絡-DAN 《利用深度適應網絡學習可遷移特徵》 下面分爲五個部分來講解： 一. 研究背景 二. 本論文所解決的問題 三. DAN 方法 四. 實驗部分 五. 結合自己的論文 一.研究背景 精簡的說， 研究表明：深度 神經網絡可以學習可遷移特徵，這些特徵用於域適應時在新的任務上表現出很好的泛化能力。然而由於深度特徵隨着網
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相關文章
- 1. Learning Transferable Features with Deep Adaptation Networks
- 2. 翻譯「Learning Transferable Features with Deep Adaptation Networks」
- 3. How transferable are features in deep nerual networks? 【paper review】
- 4. How transferable are features in deep neural networks?
- 5. [解讀] Deep Clustering for Unsupervised Learning of Visual Features
- 6. Unsupervised Domain Adaptation with Residual Transfer Networks（2017）
- 7. Dense biased networks with deep priori anatomy and hard region adaptation: Semi-supervised 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. 論文閱讀筆記（四十）：Learning Spatiotemporal Features with 3D Convolutional Networks（C3D）