Understanding and Improving Convolutional Neural Networks via Concatenated Rectified Linear Units閱讀筆
摘要 本文旨在深入研究卷積神經網絡的特性，以及一種改進cnn結構性能的通用方法。具體而言，論文檢查現有的cnn模型，並觀察到一個有趣的性質，即低層中的濾波器形成對(即相反相位的濾波器)。在我們的觀察的啓發下，我們提出了一種新的、簡單而有效的激活方案-CReLU，並從理論上分析了它在CNN中的重構性質。我們將Crelu集成到幾個最先進的cnn架構中，並在CIFAR-10/100和ImageNet數據
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相關文章
- 1. Understanding and Improving Convolutional Neural Networks via Concatenated Rectified Linear Units
- 2. Visualizing and Understanding Convolutional Networks閱讀筆記
- 3. 論文閱讀筆記（四十二）：Visualizing and Understanding Convolutional Networks
- 4. 《Visualizing and Understanding Convolutional Networks》閱讀理解
- 5. Understanding and Visualizing Convolutional Neural Networks
- 6. [ZFNet論文閱讀] Visualizing and Understanding Convolutional Networks
- 7. Understanding Convolutional Neural Networks for NLP
- 8. [轉] Understanding Convolutional Neural Networks for NLP
- 9. Visualizing and Understanding Convolutional Networks
- 10. 論文閱讀筆記（五十三）：Understanding Deep Convolutional Networks