Answers.Body: <p>It is assumed that NNs build up a hierarchical representation, whereby each layer combines features from the lower-level layers. The layers could be understood as representing a cascade of stacked features:</p>
  <p>edges -&gt; texture -&gt; patterns -&gt; parts -&gt; objects</p>
  <p>So from lower-level patterns to the more abstract higher-level concept like representation. This <a href="https://distill.pub/2017/feature-visualization/" rel="nofollow noreferrer">Distill article</a> as far as I can tell is one of the most cited sources (740 citations) and provides an in-depth explanation of the features and how to visualize them. The journal is peer-reviewed.</p>
  <p>The post also points to some older references such as: <a href="https://www.researchgate.net/publication/265022827_Visualizing_Higher-Layer_Features_of_a_Deep_Network" rel="nofollow noreferrer">this</a>, <a href="https://arxiv.org/pdf/1312.6034.pdf" rel="nofollow noreferrer">this</a> or <a href="https://www.auduno.com/2015/07/29/visualizing-googlenet-classes/" rel="nofollow noreferrer">this</a>.   The <a href="https://colah.github.io/" rel="nofollow noreferrer">website of Chris Olah</a> one of the authors of the Distill article is also a great source for finding visualizations for different deep learning architectures.</p>
  
Answers.ContentLicense: CC BY-SA 4.0
Answers.CreationDate: 2022-08-22T07:40:00.647Z
Answers.LastActivityDate: 2022-08-22T07:40:00.647Z
Answers.UserDisplayName: Mariusmarten
Body: <p>Let's consider a deep convolutional network. It seems that there is some consensus on the following notions:</p>
  <p><strong>1. Shallow layers tend to recognise more low-level features such as edges and curves.</strong></p>
  <p><strong>2. Deeper layers tend to recognise more high-level features (whatever this means).</strong></p>
  <p>While I usually come across various online articles and blogs that state this, no one ever cites literature that supports this claim. I am not seeking the question as to why this phenomenon happens, I'm only seeking whether it has actually been experimented on and documented. Also, I am barely able to find any peer-reviewed literature that provides evidence of this on sites such as Google Scholar or ResearchGate.</p>
  <p>Could anyone point me to the right direction?</p>
  
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2022-08-22T07:45:51.917Z
Comments.Text: https://arxiv.org/abs/2106.14587 is the most exhausting, most fundamental work on this - it connects the architecture of NN with neural manifolds (manifolds of activities) and with categories of theories and languages. All is one system, layers (graphs, neural manifolds, languages-theories) are category theoretic fibrations. Just define learnability meatrics on this structurue and this can solve the problem of finding the optimal architecutre for the task.
Comments.UserDisplayName: TomR
Tags: <deep-learning><convolutional-neural-networks><reference-request><features><representation-learning>
Title: Is there any proper literature on the types of features that different layers of a deep neural network learn?
UserDisplayName: mesllo
ThreadText: # Is there any proper literature on the types of features that different layers of a deep neural network learn?
  
  (Asked by: username_0 on 2022-06-28)
  
  Let's consider a deep convolutional network. It seems that there is some consensus on the following notions:
  
  **1. Shallow layers tend to recognise more low-level features such as edges and curves.**
  
  **2. Deeper layers tend to recognise more high-level features (whatever this means).**
  
  While I usually come across various online articles and blogs that state this, no one ever cites literature that supports this claim. I am not seeking the question as to why this phenomenon happens, I'm only seeking whether it has actually been experimented on and documented. Also, I am barely able to find any peer-reviewed literature that provides evidence of this on sites such as Google Scholar or ResearchGate.
  
  Could anyone point me to the right direction?
  
  **Tags:** deep-learning, convolutional-neural-networks, reference-request, features, representation-learning
  > **Comments:**
  > **username_1**: https://arxiv.org/abs/2106.14587 is the most exhausting, most fundamental work on this - it connects the architecture of NN with neural manifolds (manifolds of activities) and with categories of theories and languages. All is one system, layers (graphs, neural manifolds, languages-theories) are category theoretic fibrations. Just define learnability meatrics on this structurue and this can solve the problem of finding the optimal architecutre for the task.  
  
  ---
  
  ## 1 answers
  
  ### Answer by username_2 on 2022-08-22. Score: 1
  
  It is assumed that NNs build up a hierarchical representation, whereby each layer combines features from the lower-level layers. The layers could be understood as representing a cascade of stacked features:
  
  edges -> texture -> patterns -> parts -> objects
  
  So from lower-level patterns to the more abstract higher-level concept like representation. This [Distill article](https://distill.pub/2017/feature-visualization/) as far as I can tell is one of the most cited sources (740 citations) and provides an in-depth explanation of the features and how to visualize them. The journal is peer-reviewed.
  
  The post also points to some older references such as: [this](https://www.researchgate.net/publication/265022827_Visualizing_Higher-Layer_Features_of_a_Deep_Network), [this](https://arxiv.org/pdf/1312.6034.pdf) or [this](https://www.auduno.com/2015/07/29/visualizing-googlenet-classes/). The [website of Chris Olah](https://colah.github.io/) one of the authors of the Distill article is also a great source for finding visualizations for different deep learning architectures.
  
  
  ---
  
  