自编码器网络和全卷积网络的区别 [英] Differene between Autoencoder Network and Fully Convolution Network

问题描述

自动编码器网络和全卷积网络之间的主要区别是什么?请帮我理解这两种网络架构的区别?

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

推荐答案

自编码器至少有一个隐藏的全连接层，它通常被称为代码、潜在变量或潜在表示"维基百科.实际上，自动编码器根本不必是卷积网络 - 维基百科只声明它们是供稿-转发非循环网络.

Autoencoders have at least one hidden fully connected layer which "is usually referred to as code, latent variables, or latent representation" Wikipedia. Actually, autoencoders do not have to be convolutional networks at all - Wikipedia only states that they are feed-forward non-recurrent networks.

另一方面，全卷积网络没有任何全连接层.请参阅维基百科和Cicek 等人的这篇论文. 了解更多细节(该论文对网络进行了很好的可视化).

On the other hand, Fully Convolutional Networks do not have any fully connected layers. See Wikipedia and this paper by Cicek et al. for more details (the paper has a nice visualization of the network).

因此，即使自动编码器网络中的编码器和解码器都是 CNN，它们之间也至少有一个全连接的隐藏层.因此，自编码器网络不是 FCN.

So even when both an encoder and a decoder in an autoencoder network are CNNs, there is at least one fully-connected hidden layer in between them. Thus, autoencoder networks are not FCNs.

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