【论文笔记】Object Contour Detection with a Fully Convolutional Encoder-Decoder Network

Object Contour Detection with a Fully Convolutional Encoder-Decoder Network

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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-激活-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

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训练：
During training, we fix the encoder parameters (VGG-16) and only optimize decoder parameters.

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