What is feature embedding in the context of convolutional neural networks? What are feature embeddings in the context of convolutional neural networks? Is it related to bottleneck features or feature vectors?
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 (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 (2019)