自动编码器网络与全卷积网络的区别 [英] Differene between Autoencoder Network and Fully Convolution Network

问题描述

自动编码器网络与完全卷积网络之间的主要区别是什么？
请帮助我理解这两个网络架构之间的区别？

解决方案

1] AutoEncoder：

• Autoencodder是一种降维技术


• 它有两个部分，一个编码器和解码器


• Enocder将原始数据映射到隐藏表示（潜在空间表示）


• 解码器将隐藏表示映射回原始数据


• 网络自动学习这个隐藏的表示，结果证明这个隐藏的表示是数据最重要的特征


• 所以原始数据作为输入到编码器并且它输出潜在空间表示


• 现在编码器的输出是解码器的输入，它将尝试重新生成原始数据


• 自动编码器是无监督学习的类型



• 下面是使用卷积的自动编码器图编码器网络中的层和解码器网络中的解卷积层

  
  
  

2] 卷积网络：

• 卷积网络用于图像


• 在卷积网络中使用过滤器集合进行卷积


• 过滤器在数据中共享


• 这意味着您的数据共享权重


• 过滤器的每个像素值都是权重


• 要知道卷积操作，请查看此项链接




• 在上图中，分类器通常是完全连接的层



• 但是没有必要将完全连接的层作为分类器..你可以使用其他分类器


• 你可以参考这个来理解卷积网络和MLP网络之间的区别：
  
  

  what is the main difference between autoencoder networks and fully convolutional network? Please help me understand the difference between architecture of these two networks?

  解决方案

  1] AutoEncoder :

  • Autoencodder is a dimensionality reduction technique
  • It has two parts an encoder and decoder
  • Enocder maps the raw data to a hidden representation (Latent Space Representation)
  • Decoder maps the hidden representation back to raw data
  • The network automatically learns this hidden representation and it turns out that this hidden representation are the most important feature of your data
  • So The raw data goes as input to the encoder and it outputs the Latent Space Representation
  • Now the output of the encoder is the input to the decoder which will try to regenerate the raw data
  • Autoencoder is type of unsupervised learning

  • Below is a diagram of Autoencoder that uses convolution layer in encoder network and deconvolution layer in decoder network

  2] Convolution Network:

  • Convolution Network are used for images
  • in convolution network set of filters are used to convolve
  • The filters are shared across the data
  • Which means that you data shares weights
  • Each pixel value of your filter is a weight
  • To know the convolution operation check out this link https://www.youtube.com/watch?v=C_zFhWdM4ic
  • Every convolution layer does this convolution operation
  • Convolutional networks are type of supervised learning
  • Convolutional networks use Pooling operations for down sampling

  • Below is diagram of convolution network

  • In the above diagram usually the classifier is a fully connected layer

  • But it is not necessary to have fully connected layer as a classifier .. you can use other classifiers
  • You can refer this for understanding difference between convolutional networks and MLP network: http://cs231n.github.io/convolutional-networks/

  3] Fully Connected Layers:

  • These are simple layers with neurons
  • Each neuron has a set of weights based on the input
  • Below is the diagram of Fully connected layer

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