How transferable are features in deep neural networks?
1.背景和動機 本文全篇通過實驗說明神經網絡的可遷移性，通篇沒有一個公式，但是卻是2014年NIPS的oral presentation。 本文實驗使用12年ImageNet大賽的冠軍模型AlexNet進行了一系列關於網絡各個層級上的可遷移性的實驗。因爲CNN在提取特徵時，前面幾層學習到的都是邊邊角角，線條這樣的可以泛化的特徵，而隨着層級上升，慢慢地特徵變得specific。那麼問題來了，當我們打
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相關文章
- 1. How transferable are features in deep nerual networks? 【paper review】
- 2. How transferable are features in deep neural networks? 論文筆記
- 3. 論文筆記：How transferable are features in deep neural networks?
- 4. Learning Transferable Features with Deep Adaptation Networks
- 5. 翻譯「Learning Transferable Features with Deep Adaptation Networks」
- 6. How powerful are graph neural networks?
- 7. HOW POWERFUL ARE GRAPH NEURAL NETWORKS?
- 8. How convolution neural networks work
- 9. 對於DAN方法的解讀-Learning Transferable Features with Deep Adaptation Networks
- 10. Improving Deep Neural Networks