<p>Autoencoders have at least one hidden fully connected layer which "is usually referred to as code, latent variables, or latent representation" <a href="https://en.wikipedia.org/wiki/Autoencoder" rel="nofollow noreferrer">Wikipedia</a>.
Actually, autoencoders do not have to be convolutional networks at all - <a href="https://en.wikipedia.org/wiki/Autoencoder" rel="nofollow noreferrer">Wikipedia</a> only states that they are feed-forward non-recurrent networks.</p>

<p>On the other hand, Fully Convolutional Networks do not have any fully connected layers. See <a href="https://en.wikipedia.org/wiki/U-Net" rel="nofollow noreferrer">Wikipedia</a> and <a href="https://arxiv.org/pdf/1606.06650.pdf" rel="nofollow noreferrer">this paper by Cicek et al.</a> for more details (the paper has a nice visualization of the network).</p>

<p>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.</p>
Differene between Autoencoder Network and Fully Convolution Network
<p>what is the main difference between <strong>autoencoder networks</strong> and <strong>fully convolutional network</strong>? 
Please help me understand the difference between architecture of these two networks?</p>