  Classical supervised classification tasks search for a nonlinear mapping that
maps each encoded feature directly to a probability mass over the labels. Such
a learning framework typically lacks the intuition that encoded features from
the same class tend to be similar and thus has little interpretability for the
learned features. In this paper, we propose a novel supervised learning model
named Supervised-Encoding Quantizer (SEQ). The SEQ applies a quantizer to
cluster and classify the encoded features. We found that the quantizer provides
an interpretable graph where each cluster in the graph represents a class of
data samples that have a particular style. We also trained a decoder that can
decode convex combinations of the encoded features from similar and different
clusters and provide guidance on style transfer between sub-classes.
