翻譯「Learning Transferable Features with Deep Adaptation Networks」
利用深度適應網絡學習可遷移特徵 摘 要：最近的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絡層數的增加由一般到特殊轉變，特徵的可遷移能力在網絡高層急劇下降，極大地增加了域之間的差異性。因此減少不同數據集間的偏差，同時增加任務特徵層的可遷移能力是非常重要的。在本文中，我們提出了一個新的深度適應網絡（DAN)架構，把深度卷積神經
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相關文章
- 1. Learning Transferable Features with Deep Adaptation Networks
- 2. 對於DAN方法的解讀-Learning Transferable Features with Deep Adaptation Networks
- 3. How transferable are features in deep nerual networks? 【paper review】
- 4. [翻譯]Learning Multiple Tasks with Deep Relationship Networks
- 5. How transferable are features in deep neural networks?
- 6. 《Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks》 論文翻譯筆記
- 7. A Survey on Deep Transfer Learning 2018 翻譯
- 8. [翻譯]Learning Deep Features for Discriminative Localization
- 9. ImageNet Classification with Deep Convolutional Neural Networks （AlexNet）翻譯
- 10. [翻譯]FlowNet: Learning Optical Flow with Convolutional Networks