Comments (4)
FCN与CNN的区别
FCN的最后几层不是全连接层,而CNN是全连接层。这些全连接层都是一维的信息,丢失了二维的信息。
CNN是图像到结果的网络,从二维信息到一维信息,一般输出一个图片的结果。而FCN网络是从图像到图像的网络,从二维信息到二维信息,是一个像素级的网络,对应每个像素点的结果。
CNN的识别是图像级的识别,也就是从图像到结果,而FCN的识别是像素级的识别,对输入图像的每一个像素在输出上都有对应的判断标注,标明这个像素最可能是属于一个什么物体/类别。
上采样和下采用
CNN从高维度到低维度,卷积+池化的过程就是下采用。FCN从低维度到高维度,反卷积的过程就是上采样。
bilinear interpolation 双线性内插
感受野
这里就涉及到一个感受野(receptive field)的概念。较浅的卷积层(靠前的)的感受域比较小,学习感知细节部分的能力强,较深的隐藏层(靠后的),感受域相对较大,适合学习较为整体的,相对更宏观一些的特征。
感受野被定义为特定 CNN 特征正在“看”(即受其影响)的输入空间中的区域。特征的感受野可以通过其中心位置及其大小进行充分描述。
所以在做反卷积的时候,会考虑浅层的卷积信息,辅助叠加得到更好的分割结果。
概念
- 条件随机场
- Dilated Convolutions
from deep-learning-on-medical-image.
FCN
Introduction
Title: Fully Convolutional Networks for Semantic Segmentation
Author: Jonathan Long, Evan Shelhamer, Trevor Darrell, UC Berkeley
arXiv: 1411.4038
2015 CVPR Best Paper Honorable Mention
#Keyword
- semantic segmentation(语义分割)
- end-to-end, pixels-to-pixels(端到端,像素级)
- fully convolutional networks
(1) 将最后几层全连接层改为卷积层,大小为原来1d全连接层进行resize(故称“全卷积网络”)
(2) 将入了转置卷积,并与浅层池化层输出结合,通过设置stride来放大
Main work
- make predition a every pixel
- 全连接层解释为卷积层,fine-tune
- upsampleing(eg. bilinear upsampling) (TODO)
内插法,上采样(多的位置填0)
from deep-learning-on-medical-image.
Deeplab
from deep-learning-on-medical-image.
SegNet
from deep-learning-on-medical-image.
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from deep-learning-on-medical-image.