论文阅读理解 - Dilated Convolution

最新推荐文章于 2024-01-11 11:32:49 发布

AIHGF

最新推荐文章于 2024-01-11 11:32:49 发布

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分类专栏：论文阅读 CaffeLayer 文章标签：论文阅读 Caffe

本文链接：https://blog.youkuaiyun.com/zziahgf/article/details/77947565

本文深入探讨了Dilated Convolution，它在保持高分辨率的同时，能够整合多尺度内容信息，尤其适用于语义分割任务。通过Dilated Convolution，网络可以在不减少分辨率的情况下实现接收野的指数增长。文中详细介绍了Caffe中Dilated Convolution的实现，并引用了一篇相关论文，阐述了其如何增强context模块以提升dense prediction的准确性。

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Dilated Convolution

[Paper]: Multi-scale Context Aggregation by Dilated Convolutions

[Caffe-Code]

1. Caffe 中的定义

Dilated Convolution 已经可在 Caffe 官方的卷积层参数中定义.

message ConvolutionParameter {
  // Factor used to dilate the kernel, (implicitly) zero-filling the resulting holes. 
  // (Kernel dilation is sometimes referred to by its use in the
  //  algorithme à trous from Holschneider et al. 1987.)
  repeated uint32 dilation = 18; // The dilation; defaults to 1
}

layer {
  name: "ct_conv1_1"
  type: "Convolution"
  bottom: "fc-final"
  top: "ct_conv1_1"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  param {
    lr_mult: 2
    decay_mult: 1
  }
  convolution_param {
    num_output: 42
    pad: 33
    kernel_size: 3
  }
}
layer {
  name: "ct_relu1_1"
  type: "ReLU"
  bottom: "ct_conv1_1"
  top: "ct_conv1_1"
}
layer {
  name: "ct_conv1_2"
  type: "Convolution"
  bottom: "ct_conv1_1"
  top: "ct_conv1_2"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  param {
    lr_mult: 2
    decay_mult: 1
  }
  convolution_param {
    num_output: 42
    pad: 0
    kernel_size: 3
  }
}
layer {
  name: "ct_relu1_2"
  type: "ReLU"
  bottom: "ct_conv1_2"
  top: "ct_conv1_2"
}
layer {
  name: "ct_conv2_1"
  type: "Convolution"
  bottom: "ct_conv1_2"
  top: "ct_conv2_1"
  convolution_param {
    num_output: 84
    kernel_size: 3
    dilation: 2
  }
}
layer {
  name: "ct_relu2_1"
  type: "ReLU"
  bottom: "ct_conv2_1"
  top: "ct_conv2_1"
}
layer {
  name: "ct_conv3_1"
  type: "Convolution"
  bottom: "ct_conv2_1"
  top: "ct_conv3_1"
  convolution_param {
    num_output: 168
    kernel_size: 3
    dilation: 4
  }
}
layer {
  name: "ct_relu3_1"
  type: "ReLU"
  bottom: "ct_conv3_1"
  top: "ct_conv3_1"
}
layer {
  name: "ct_conv4_1"
  type: "Convolution"
  bottom: "ct_conv3_1"
  top: "ct_conv4_1"
  convolution_param {
    num_output: 336
    kernel_size: 3
    dilation: 8
  }
}
layer {
  name: "ct_relu4_1"
  type: "ReLU"
  bottom: "ct_conv4_1"
  top: "ct_conv4_1"
}
layer {
  name: "ct_conv5_1"
  type: "Convolution"
  bottom: "ct_conv4_1"
  top: "ct_conv5_1"
  convolution_param {
    num_output: 672
    kernel_size: 3
    dilation: 16
  }
}
layer {
  name: "ct_relu5_1"
  type: "ReLU"
  bottom: "ct_conv5_1"
  top: "ct_conv5_1"
}
layer {
  name: "ct_fc1"
  type: "Convolution"
  bottom: "ct_conv5_1"
  top: "ct_fc1"
  convolution_param {
    num_output: 672
    kernel_size: 3
  }
}
layer {
  name: "ct_fc1_relu"
  type: "ReLU"
  bottom: "ct_fc1"
  top: "ct_fc1"
}
layer {
  name: "ct_final"
  type: "Convolution"
  bottom: "ct_fc1"
  top: "ct_final"
  convolution_param {
    num_output: 21
    kernel_size: 1
  }
}