Very Deep Convolutional Networks for Large-scale Image Recognition(vggnet)

2024-05-13 01:09:55

vggNet是从AlexNet而来，主要探索卷积神经网络的深度与性能之间的关系，通过反复堆叠3x3的卷积核（c中有1x1的卷积核,也只有c中有，c是16层）和2x2的最大池化层，vggNet构筑了16-19层深的卷积神经网络。

3x3卷积核：the smallest size to capture the notion of left/right,up/down,center

1x1卷积核：can be seen as a linear transformation of th input channels(followed by non-linearity)

整个网络的convolution stride固定为1，所有隐藏层的激活函数都用relu。

vggNet网络堆叠stride为1的3x3卷积核。两个3x3的卷积核堆叠的局部感受野相当于一个5x5的局部感受野，3个3x3的卷积核堆叠的局部感受野相当于7x7的局部感受野。堆叠的3x3卷积核比直接使用大的卷积核有两个好处：

1.增加非线性，因为每一层卷积核都有非线性激活函数relu，3层的3x3卷积核比7x7卷积核多进行了两次非线性变换，这样使得decision function more discriminative

2.减少了参数。假设通道数为c，3层3x3卷积核的参数量是3*（3*3*C*C），1层7x7卷积核的参数量是7*7*C*C。

vggNet中的1x1卷积核：GoogLeNet也采用了1x1卷积核，但不同的是，vggNet目的是增加非线性，而没有降维的目的。vggNet中要求1x1卷积核的输入输出维度应相等。

http://blog.csdn.net/wcy12341189/article/details/56281618 讲解vgg

http://blog.csdn.net/u014114990/article/details/51125776 多通道参数量的计算

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