  In this paper, a novel confidence conditioned knowledge distillation (CCKD)
scheme for transferring the knowledge from a teacher model to a student model
is proposed. Existing state-of-the-art methods employ fixed loss functions for
this purpose and ignore the different levels of information that need to be
transferred for different samples. In addition to that, these methods are also
inefficient in terms of data usage. CCKD addresses these issues by leveraging
the confidence assigned by the teacher model to the correct class to devise
sample-specific loss functions (CCKD-L formulation) and targets (CCKD-T
formulation). Further, CCKD improves the data efficiency by employing
self-regulation to stop those samples from participating in the distillation
process on which the student model learns faster. Empirical evaluations on
several benchmark datasets show that CCKD methods achieve at least as much
generalization performance levels as other state-of-the-art methods while being
data efficient in the process. Student models trained through CCKD methods do
not retain most of the misclassifications commited by the teacher model on the
training set. Distillation through CCKD methods improves the resilience of the
student models against adversarial attacks compared to the conventional KD
method. Experiments show at least 3% increase in performance against
adversarial attacks for the MNIST and the Fashion MNIST datasets, and at least
6% increase for the CIFAR10 dataset.
