How transferable are features in deep neural networks? 論文筆記
0 摘要 在自然圖像上訓練的深度神經網絡都表現出一種奇怪的現象：模型第一層的參數和Gabor filters的參數非常相似（也就是說模型擁有經典圖像算法中具有的優秀性質，Gabor filter是比較通用的濾波器，也就是說模型在一開始也要完成一些常規模式的計算過程，這一點和經典圖像算法是一樣的）。第一層的特徵並不是特定於某一數據集或者某一任務，而是通用的特徵，它們適用於許多數據集和普遍的任
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相關文章
- 1. How transferable are features in deep neural networks?
- 2. 論文筆記：How transferable are features in deep neural networks?
- 3. How transferable are features in deep nerual networks? 【paper review】
- 4. Learning Transferable Features with Deep Adaptation Networks
- 5. 翻譯「Learning Transferable Features with Deep Adaptation Networks」
- 6. Understanding Neural Networks Through Deep Visualization 論文筆記
- 7. [論文筆記]Deep Neural Networks are Easily Fooled: High Confidence Predictions for Unrecognizable Images
- 8. How powerful are graph neural networks?
- 9. HOW POWERFUL ARE GRAPH NEURAL NETWORKS?
- 10. Neural Networks and Deep Learning-引論