  Knowledge Transfer (KT) achieves competitive performance and is widely used
for image classification tasks in model compression and transfer learning.
Existing KT works transfer the information from a large model ("teacher") to
train a small model ("student") by minimizing the difference of their
conditionally independent output distributions. However, these works overlook
the high-dimension structural knowledge from the intermediate representations
of the teacher, which leads to limited effectiveness, and they are motivated by
various heuristic intuitions, which makes it difficult to generalize. This
paper proposes a novel Contrastive Knowledge Transfer Framework (CKTF), which
enables the transfer of sufficient structural knowledge from the teacher to the
student by optimizing multiple contrastive objectives across the intermediate
representations between them. Also, CKTF provides a generalized agreement to
existing KT techniques and increases their performance significantly by
deriving them as specific cases of CKTF. The extensive evaluation shows that
CKTF consistently outperforms the existing KT works by 0.04% to 11.59% in model
compression and by 0.4% to 4.75% in transfer learning on various models and
datasets.
