What are feature embeddings in the context of Convolutional Neural Networks? Is it related to bottleneck features or feature vectors?
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.