Learning Transferable Features with Deep Adaptation Networks
論文相關內容 本文中域適應的方法及目的 haI最近的研究表明，深度神經網絡可以學習可轉移的特徵，這些特徵可以很好地推廣到新的領域適應任務。然而，隨着深度特徵在網絡中最終由一般特徵向特定特徵過渡，隨着區域差異的增大，深度特徵在更高層次上的可移植性顯著下降。因此，形式化地減少數據集偏差，增強任務特定層的可移植性是非常重要的。本文提出了一種新的深度適應方法網絡(DAN)結構，將深度卷積神經網絡推廣到領域
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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. 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）