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Learning Transferable Features with Deep Adaptation Networks
時間 2020-12-23
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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)
>>更多相關文章<<