Body: <p>I'm familiar with Auto-Encoders and I'm about to dive into CNNs. By having a look at the most important component of a CNN, the filter:</p>
  <p><a href="https://i.sstatic.net/eju7Y.gif" rel="nofollow noreferrer"><img src="https://i.sstatic.net/eju7Y.gif" alt="img1" /></a></p>
  <p>I wonder how it is different from Auto-Encoders:</p>
  <p><a href="https://i.sstatic.net/dqf0U.png" rel="nofollow noreferrer"><img src="https://i.sstatic.net/dqf0U.png" alt="img2" /></a></p>
  <p>For me, it looks conceptually the same. I even have to admit it is quite of another higher concept: Dimensionality reduction. In e.g. PCA, as well as in AE and in CNN(?) you transform higher data / higher dimensions onto lower / compressed data.
  I can see that the methods are somehow different but yet, can't really explain in which manner, finally.</p>
  
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2021-06-18T12:18:59.833Z
Comments.Text: CNN is known to be good at extracting features from images, which is why auto encoders utilize CNN for the encoding part.
Comments.UserDisplayName: Aray Karjauv
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2021-06-21T06:36:57.747Z
Comments.Text: Ok, interesting..  so, in this context, CNN is half an AE.
Comments.UserDisplayName: Ben
Tags: <convolutional-neural-networks><comparison><autoencoders>
Title: What is the conceptual difference between convolutional neural networks and auto-encoders?
UserDisplayName: Ben
ThreadText: # What is the conceptual difference between convolutional neural networks and auto-encoders?
  
  (Asked by: username_0 on 2021-06-18)
  
  I'm familiar with Auto-Encoders and I'm about to dive into CNNs. By having a look at the most important component of a CNN, the filter:
  
  [![img1](https://i.sstatic.net/eju7Y.gif)](https://i.sstatic.net/eju7Y.gif)
  
  I wonder how it is different from Auto-Encoders:
  
  [![img2](https://i.sstatic.net/dqf0U.png)](https://i.sstatic.net/dqf0U.png)
  
  For me, it looks conceptually the same. I even have to admit it is quite of another higher concept: Dimensionality reduction. In e.g. PCA, as well as in AE and in CNN(?) you transform higher data / higher dimensions onto lower / compressed data.
  I can see that the methods are somehow different but yet, can't really explain in which manner, finally.
  
  **Tags:** convolutional-neural-networks, comparison, autoencoders
  
  > **Comments:**
  > **username_1**: CNN is known to be good at extracting features from images, which is why auto encoders utilize CNN for the encoding part.  
  > **username_0**: Ok, interesting..  so, in this context, CNN is half an AE.  
  
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  ## 0 answers
  
  