Understanding and Improving Convolutional Neural Networks via Concatenated Rectified Linear Units阅读笔
摘要 本文旨在深入研究卷积神经网络的特性，以及一种改进cnn结构性能的通用方法。具体而言，论文检查现有的cnn模型，并观察到一个有趣的性质，即低层中的滤波器形成对(即相反相位的滤波器)。在我们的观察的启发下，我们提出了一种新的、简单而有效的激活方案-CReLU，并从理论上分析了它在CNN中的重构性质。我们将Crelu集成到几个最先进的cnn架构中，并在CIFAR-10/100和ImageNet数据
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相关文章
- 1. Understanding and Improving Convolutional Neural Networks via Concatenated Rectified Linear Units
- 2. Visualizing and Understanding Convolutional Networks阅读笔记
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