  This effort aims to reproduce the results of experiments and analyze the
robustness of the review framework for knowledge distillation introduced in the
CVPR '21 paper 'Distilling Knowledge via Knowledge Review' by Chen et al.
Previous works in knowledge distillation only studied connections paths between
the same levels of the student and the teacher, and cross-level connection
paths had not been considered. Chen et al. propose a new residual learning
framework to train a single student layer using multiple teacher layers. They
also design a novel fusion module to condense feature maps across levels and a
loss function to compare feature information stored across different levels to
improve performance. In this work, we consistently verify the improvements in
test accuracy across student models as reported in the original paper and study
the effectiveness of the novel modules introduced by conducting ablation
studies and new experiments.
