Object Contour Detection with a Fully Convolutional Encoder-Decoder Network
使用卷积编码解码网络检测主要目标的边缘
网络结构是：
编码：VGG-16
解码：反池化-卷积-**-dropout
卷积核:
The number of channels of every decoder layer is properly
designed to allow unpooling from its corresponding maxpooling layer.
dropout:
We also add a dropout layer after each relu layer in the decoder. A complete decoder network setup is listed in Table 1
损失函数：
loss function is simply the pixel-wise logistic loss.
反池化：
We initialize our encoder with VGG-16 net [48] (up to the “fc6” layer) and to achieve dense prediction of image size our decoder is constructed by alternating unpooling and convolution layers where unpooling layers re-use the switches from max-pooling layers of encoder to upscale the feature maps.
反池化细节：
http://blog.csdn.net/xiaxzhou/article/details/74012137
训练：
During training, we fix the encoder parameters (VGG-16) and only optimize decoder parameters.