Weighted ChannelDropoutfor Regularization of Deep Convolutional Neural Network论文记录
简介 近年来，卷积神经网络实现了很大的发展，这已经显著提升了很多不同应用的性能表现，深度卷积神经网络可以成功的原因很大程度是因为其多个非线性隐藏层的结构，其中包含了数以百万计的参数，因此可以学习输入和输出之间的关系。 作者观察提出了一种新的方法，称为加权信道丢失率(WCD)的正则化深卷积神经网络(CNN)。在一个卷积神经网络的卷积堆栈内，所有的通道都是由之前的层生成的，并会在下一层得到平等的对待，
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