Surpassing Human-Level Performance on ImageNet Classification ImageNet Classification
在该文章的两大创新点：一个是PReLU,一个是权值初始化的方法。下面我们分别一一来看。 PReLU(paramter ReLU) 所谓的PRelu，即在 ReLU激活函数的基础上加入了一个参数，看一个图就明白了： 右边的图上多了一个参数吧，在负半轴上不再为0，而是一个可以学习的斜率。 很容易明白。实验结果显示该方法可以提高识别率。 权值初始化的方法： 对于文中的权值初始化问题：主
相关文章
- 1. PReLU——Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification
- 2. [Kaiming]Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification
- 3. Pre-training on Grayscale ImageNet Improves Medical Image Classification
- 4. ImageNet Classification with Deep Convolutional Neural Networks
- 5. DenseNet Models for Tiny ImageNet Classification
- 6. 《ImageNet Classification with Deep Convolutional Neural Network》
- 7. 读文献——《ImageNet classification with deep convolutional neural networks》
- 8. 【Imagenet LSVRC系列模型】Alexnet-2012 Imagenet Classification with Deep Convolutionnal Neural Networks
- 9. ImageNet Classification with Deep Convolutional Neural Networks -- 解读
- 10. AlexNet--ImageNet Classification with Deep Convolutional Neural Networks
- 更多相关文章...
- • R 包 - R 语言教程
- • ionic 手势事件 - ionic 教程
- • RxJava操作符（一）Creating Observables
- • PHP Ajax 跨域问题最佳解决方案
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. PReLU——Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification
- 2. [Kaiming]Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification
- 3. Pre-training on Grayscale ImageNet Improves Medical Image Classification
- 4. ImageNet Classification with Deep Convolutional Neural Networks
- 5. DenseNet Models for Tiny ImageNet Classification
- 6. 《ImageNet Classification with Deep Convolutional Neural Network》
- 7. 读文献——《ImageNet classification with deep convolutional neural networks》
- 8. 【Imagenet LSVRC系列模型】Alexnet-2012 Imagenet Classification with Deep Convolutionnal Neural Networks
- 9. ImageNet Classification with Deep Convolutional Neural Networks -- 解读
- 10. AlexNet--ImageNet Classification with Deep Convolutional Neural Networks