Structural Design of Convolutional Neural Networks for Steganalysis
论文摘要 研究表明，卷积神经网络可能不适合于图像隐写分析。Xunet考虑了传统隐写分析的领域知识设计了 一种新的卷积神经网络的结构，在网络中采用第一层卷积中所产生的特征值的绝对值，来方便与改进后续层的统计建模(什么意思不太懂)。在网络的前几层使用TanH激活函数，后几层采用 1 × 1 1\times1 1×1的卷积核来防止过拟合。 其具体的网络结构如下图所示： 对网络的多个层的功能进行具体的解释
相关文章
- 1. Structural Design of Convolutional Neural Networks for Steganalysis 阅读笔记
- 2. Bag of Tricks for Convolutional Neural Networks
- 3. Convolutional Neural Networks---Foundations of Convolutional Neural Networks
- 4. Deep Convolutional Neural Networks for Sentiment Analysis of Short Texts
- 5. Bag of Tricks for Image Classification with Convolutional Neural Networks
- 6. Understanding Convolutional Neural Networks for NLP
- 7. [转] Understanding Convolutional Neural Networks for NLP
- 8. Pre-Training Graph Neural Networks for Generic Structural Feature Extraction
- 9. 014 Foundation of Convolutional Neural Networks
- 10. CS231n Convolutional Neural Networks for Visual Recognition
- 更多相关文章...
- • Scala for循环 - Scala教程
- • Swift for 循环 - Swift 教程
- • 使用Rxjava计算圆周率
- • 算法总结-归并排序
相关标签/搜索
每日一句
-
每一个你不满意的现在，都有一个你没有努力的曾经。
最新文章
欢迎关注本站公众号,获取更多信息
相关文章
- 1. Structural Design of Convolutional Neural Networks for Steganalysis 阅读笔记
- 2. Bag of Tricks for Convolutional Neural Networks
- 3. Convolutional Neural Networks---Foundations of Convolutional Neural Networks
- 4. Deep Convolutional Neural Networks for Sentiment Analysis of Short Texts
- 5. Bag of Tricks for Image Classification with Convolutional Neural Networks
- 6. Understanding Convolutional Neural Networks for NLP
- 7. [转] Understanding Convolutional Neural Networks for NLP
- 8. Pre-Training Graph Neural Networks for Generic Structural Feature Extraction
- 9. 014 Foundation of Convolutional Neural Networks
- 10. CS231n Convolutional Neural Networks for Visual Recognition