深度学习入门:Simultaneous Feature Learning and Hash Coding with Deep Neural Networks

             Simultaneous Feature Learning 

         and Hash Coding with Deep Neural Networks

  这篇paper也是和深度hash相关的，该论文分为三个部分，如下图所示：输入的image这里使用的是三个输入图像，有两个图想属于同一个class，另一个图像和属于另一个class。在试验部分，作者让三个image使用相同的一个网络（当然，还可以让queries使用一个net，而similar images和dissimilar images共享一个Net)达到了比较好的效果。中间一些列的卷积层其实是network-in-network的一个改变版本：在每一个卷积层的后面插入一个1*1的卷积核作为一个线性的转换器，把fc层改为average pooling，最终的输出feature map大小是50*#bits*1*1。简单点而看下面的divide-and-encode中有n个bits，每个bits中有50个神经元，然后这50个神经元在concatenate成为一个神经元，这样50*bits*1*1就变成了bits*1*1*1,也就是bits个神经元，然后使用的triple loss进行参数学习。

   

     最后得到的50个神经元在经过一个piece-wise 阈值函数如下:

 中间的网络架构如下：

 divide-and-encode 的caffe版本实现如下:(如有错误，请不吝赐教)

layer{
  name: "pool4"
  type: "Pooling"
  bottom: "conv4_1"
  top: "pool4"
  pooling_param{
    pool: AVERAGE
    kernel_size: 3
    stride: 2
  }
}


layer{
  name: "spilt"
  type: "Slice"
  bottom: "pool4"
  top: "slice_1"
  top: "slice_2"
  top: "slice_3"
  top: "slice_4"
  slice_param{
    slice_dim:1
    slice_point:50
    slice_point:100
    slice_point:150
  }
}

layer {
  name: "ip_1"
  type: "InnerProduct"
  bottom: "slice_1"
  top: "hash_1"
  inner_product_param {
    num_output: 1
  }
}

layer {
  name: "ip_2"
  type: "InnerProduct"
  bottom: "slice_2"
  top: "hash_2"
  inner_product_param {
    num_output: 1
  }
}

layer {
  name: "ip_3"
  type: "InnerProduct"
  bottom: "slice_3"
  top: "hash_3"
  inner_product_param {
    num_output: 1
  }
}

layer {
  name: "ip_4"
  type: "InnerProduct"
  bottom: "slice_4"
  top: "hash_4"
  inner_product_param {
    num_output: 1
  }
}

layer {
  name: "concat"
  type: "Concat"
  bottom: "hash_1"
  bottom: "hash_2"
  bottom: "hash_3"
  bottom: "hash_4"
  top: "concat"
  concat_param {
    concat_dim: 1
  }
}

layer {
  name: "triplet"
  type: "Slice"
  bottom: "concat"
  top: "query"
  top: "positive"
  top: "negetive"
  slice_param {
    axis: 0
    slice_point: 16
    slice_point: 32
  }
}

layer {
  name: "pos_sim"
  type: "DotProductSimilarity"
  bottom: "query"
  bottom: "positive"
  top: "pos_sim"
}
layer {
  name: "neg_sim"
  type: "DotProductSimilarity"
  bottom: "query"
  bottom: "negetive"
  top: "neg_sim"
}
layer {
  name: "loss"
  type: "PairwiseRankingLoss"
  bottom: "pos_sim"
  bottom: "neg_sim"
  top: "loss"
  triplet_loss_param {
    margin: 1
  }
}