  This work introduces a novel knowledge distillation framework for
classification tasks where information on existing subclasses is available and
taken into consideration. In classification tasks with a small number of
classes or binary detection (two classes) the amount of information transferred
from the teacher to the student network is restricted, thus limiting the
utility of knowledge distillation. Performance can be improved by leveraging
information about possible subclasses within the available classes in the
classification task. To that end, we propose the so-called Subclass Knowledge
Distillation (SKD) framework, which is the process of transferring the
subclasses' prediction knowledge from a large teacher model into a smaller
student one. Through SKD, additional meaningful information which is not in the
teacher's class logits but exists in subclasses (e.g., similarities inside
classes) will be conveyed to the student and boost its performance.
Mathematically, we measure how many extra information bits the teacher can
provide for the student via SKD framework. The framework developed is evaluated
in clinical application, namely colorectal polyp binary classification. In this
application, clinician-provided annotations are used to define subclasses based
on the annotation label's variability in a curriculum style of learning. A
lightweight, low complexity student trained with the proposed framework
achieves an F1-score of 85.05%, an improvement of 2.14% and 1.49% gain over the
student that trains without and with conventional knowledge distillation,
respectively. These results show that the extra subclasses' knowledge (i.e.,
0.4656 label bits per training sample in our experiment) can provide more
information about the teacher generalization, and therefore SKD can benefit
from using more information to increase the student performance.
