空洞卷积Atrous convolution和ASPP
1.Atrous Convolution
from: https://towardsdatascience.com/review-deeplabv1-deeplabv2-atrous-convolution-semantic-segmentation-b51c5fbde92d
The term “Atrous” indeed comes from French “à trous” meaning hole. Thus, it is also called “algorithme à trous” and “hole algorithm”. Some of the papers also call this “dilated convolution”. It is commonly used in wavelet transform and right now it is applied in convolutions for deep learning.
Below is the equation of atrous convolution:
1D Atrous Convolution (r>1: atrous convolution, r=1: standard convolution)
When r=1, it is the standard convolution we usually use.
When r>1, it is the atrous convolution which is the stride to sample the input sample during convolution.
The below figure illustrate the idea:
Standard Convolution (Top) Atrous Convolution (Bottom)
The idea of atrous convolution is simple. At the top of the figure above, it is the standard convolution.
At the bottom of the figure, it is the atrous convolution. We can see that when rate = 2, the input signal is sampled alternatively. First, pad=2 means we pad 2 zeros at both left and right sides. Then, with rate=2, we sample the input signal every 2 inputs for convolution. Thus, at the output, we will have 5 outputs which makes the output feature map larger.