Answers.Body: <p>The term <em>feature embedding</em> appears to be a synonym for <em>feature extraction, feature learning</em> etc. I.e. a form of embedding/dimension reduction (with the caveat the goal may not be a lower dimensional representation but one of equal dimensionality, but more meaningfully expressed):</p>
  
  <blockquote>
    <p>Feature embedding is an emerging research area which intends to transform features from the original space into a new space to support effective learning. </p>
  </blockquote>
  
  <ul>
  <li><a href="https://link.springer.com/article/10.1007/s10796-018-9850-y" rel="nofollow noreferrer">Generalized Feature Embedding for Supervised, Unsupervised, and Online Learning Tasks</a> (2019)</li>
  </ul>
  
  <blockquote>
    <p>Feature embedding aims to learn a low-dimensional vector
    representation for each instance to preserve the information
    in its features.</p>
  </blockquote>
  
  <ul>
  <li><a href="http://people.tamu.edu/~xhuang/Xiao_AAAI19.pdf" rel="nofollow noreferrer">Large-Scale Heterogeneous Feature Embedding</a> (2019)</li>
  </ul>
  
Answers.ContentLicense: CC BY-SA 4.0
Answers.CreationDate: 2020-04-27T18:58:57.613Z
Answers.LastActivityDate: 2020-04-27T19:12:32.987Z
Answers.LastEditDate: 2020-04-27T19:12:32.987Z
Answers.UserDisplayName: brazofuerte
Answers.Body: <blockquote>
    <p>Feature embeddings are basically anything that can act as a hidden representation for given object. </p>
  </blockquote>
  
  <p>In the case of images, a CNN architecture is built to create such hidden representation. Usually, the outcome of the bottleneck layer is flattened (and sometimes, converted to even lower dimensional space by adding one more dense layer) and used as feature embeddings.</p>
  
Answers.ContentLicense: CC BY-SA 4.0
Answers.CreationDate: 2018-06-09T06:42:44.327Z
Answers.LastActivityDate: 2018-06-09T06:42:44.327Z
Answers.UserDisplayName: Suyog Jadhav
Body: <p>What are feature embeddings in the context of convolutional neural networks? Is it related to bottleneck features or feature vectors?</p>
  
Tags: <convolutional-neural-networks><terminology><representation-learning>
Title: What is feature embedding in the context of convolutional neural networks?
UserDisplayName: Kaustubh
ThreadText: # What is feature embedding in the context of convolutional neural networks?
  
  (Asked by: username_0 on 2018-05-29)
  
  What are feature embeddings in the context of convolutional neural networks? Is it related to bottleneck features or feature vectors?
  
  **Tags:** convolutional-neural-networks, terminology, representation-learning
  
  ---
  
  ## 2 answers
  
  ### Answer by username_1 on 2020-04-27. Score: 5
  
  The term *feature embedding* appears to be a synonym for *feature extraction, feature learning* etc. I.e. a form of embedding/dimension reduction (with the caveat the goal may not be a lower dimensional representation but one of equal dimensionality, but more meaningfully expressed):
  
  > Feature embedding is an emerging research area which intends to transform features from the original space into a new space to support effective learning.
  
  * [Generalized Feature Embedding for Supervised, Unsupervised, and Online Learning Tasks](https://link.springer.com/article/10.1007/s10796-018-9850-y) (2019)
  
  > Feature embedding aims to learn a low-dimensional vector
  > representation for each instance to preserve the information
  > in its features.
  
  * [Large-Scale Heterogeneous Feature Embedding](http://people.tamu.edu/~xhuang/Xiao_AAAI19.pdf) (2019)
  
  
  ---
  
  ### Answer by username_2 on 2018-06-09. Score: 3
  
  > Feature embeddings are basically anything that can act as a hidden representation for given object.
  
  In the case of images, a CNN architecture is built to create such hidden representation. Usually, the outcome of the bottleneck layer is flattened (and sometimes, converted to even lower dimensional space by adding one more dense layer) and used as feature embeddings.
  
  
  ---
  
  