I am reading the paper Semi-Supervised Deep Learning with Memory (2018) by Yanbei Chen et al. The topic is the classification of images using semi-supervised learning. The authors use a term on page 2 in the middle of the page that I am not familiar with. They write:
The key to our framework design is two-aspect: (1) the class-level discriminative feature representation and the network inference uncertainty are gradually accumulated in an external memory module; (2) this memorised information is utilised to assimilate the newly incoming image samples on-the-fly and generate an informative unsupervised memory loss to guide the network learning jointly with the supervised classification loss
I am not sure what the term discriminative feature representation means.
I know that a discriminative model determines the decision boundary between the classes, and examples include: Logistic Regression (LR), Support Vector Machine (SVM), conditional random fields (CRFs) and others.
Moreover, I know that, in machine learning, feature learning or representation learning is a set of techniques that allows a system to automatically discover the representations needed for feature detection or classification from raw data.
Any insights on the definition of this term much appreciated.