Body: <p>Across the literature, the terms &quot;high-level&quot; and &quot;low-level&quot; are generally used as an adjective to the <strong>features</strong> generated by the convolution neural network as intermediate representations.</p>
  <p>Should I understand the <em>level</em> to be either high or low based on</p>
  <ol>
  <li>the position of feature maps considering the architecture of the convolutional neural network i.e., the size of the feature maps generated  at different layers of the convolutional neural network</li>
  </ol>
  <p>(For example, the feature maps after bottleneck layers are &quot;high-level&quot; and are after the wide layers are &quot;low-level&quot; )</p>
  <p><strong>or</strong></p>
  <ol start="2">
  <li>the content of the feature maps based on the task</li>
  </ol>
  <p>(For example, the feature maps that learned eyebrows of a cat are &quot;high-level&quot; and are just containing pixel intensities are &quot;low-level&quot; )</p>
  <p><strong>or</strong></p>
  <ol start="3">
  <li>any other property?</li>
  </ol>
  
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2021-09-15T09:10:13.973Z
Comments.Text: Like every question about the use of a word: please link to the paper where you read the word.
Comments.UserDisplayName: Criticize SE actions means ban
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Comments.CreationDate: 2021-09-15T10:36:25.840Z
Comments.Text: @user253751 I think it is widely known one.
Comments.UserDisplayName: hanugm
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2021-09-15T10:41:07.837Z
Comments.Text: consider that "layer" and "level" can have similar meanings in English. Is it possible the high-level features are just the features from the high levels (layers)?
Comments.UserDisplayName: Criticize SE actions means ban
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2021-09-15T13:05:34.163Z
Comments.Text: @user253751 I don't think layer, level are ambiguous here. Level is entirely different from layer. Layer is particular to neural networks and I never heard layer as level.
Comments.UserDisplayName: hanugm
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2021-09-15T16:31:51.463Z
Comments.Text: as a comment: its just a saying, and there is no real rigour definition. it is used to denote that, for example, certain "neurons" in CNNs activate higher (and seemingly uniquely) when showed certain edges, and certain "neurons" in CNNs activate higher (and seemingly uniquely) to more "difficult concepts" like faces. there is no rigour definition because we are not certain how this works
Comments.UserDisplayName: k.c. sayz 'k.c sayz'
Tags: <convolutional-neural-networks><terminology><features>
Title: What does it mean by "low-level" and "high-level" in features generated by CNN?
UserDisplayName: hanugm
ThreadText: # What does it mean by "low-level" and "high-level" in features generated by CNN?
  
  (Asked by: username_0 on 2021-09-15)
  
  Across the literature, the terms "high-level" and "low-level" are generally used as an adjective to the **features** generated by the convolution neural network as intermediate representations.
  
  Should I understand the *level* to be either high or low based on
  
  1. the position of feature maps considering the architecture of the convolutional neural network i.e., the size of the feature maps generated at different layers of the convolutional neural network
  
  (For example, the feature maps after bottleneck layers are "high-level" and are after the wide layers are "low-level" )
  
  **or**
  
  2. the content of the feature maps based on the task
  
  (For example, the feature maps that learned eyebrows of a cat are "high-level" and are just containing pixel intensities are "low-level" )
  
  **or**
  
  3. any other property?
  
  **Tags:** convolutional-neural-networks, terminology, features
  
  > **Comments:**
  > **username_1**: Like every question about the use of a word: please link to the paper where you read the word.  
  > **username_0**: @user253751 I think it is widely known one.  
  > **username_1**: consider that "layer" and "level" can have similar meanings in English. Is it possible the high-level features are just the features from the high levels (layers)?  
  > **username_0**: @user253751 I don't think layer, level are ambiguous here. Level is entirely different from layer. Layer is particular to neural networks and I never heard layer as level.  
  > **username_2**: as a comment: its just a saying, and there is no real rigour definition. it is used to denote that, for example, certain "neurons" in CNNs activate higher (and seemingly uniquely) when showed certain edges, and certain "neurons" in CNNs activate higher (and seemingly uniquely) to more "difficult concepts" like faces. there is no rigour definition because we are not certain how this works  
  
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  ## 0 answers
  
  