Knowledge Condensation Distillation

TL;DR

Knowledge Condensation Distillation (KCD) enhances traditional knowledge distillation by dynamically estimating knowledge value and iteratively condensing a compact knowledge set, leading to more efficient and effective student learning.

Contribution

KCD introduces a dynamic, EM-based framework for condensing teacher knowledge, improving distillation efficiency without extra parameters.

Findings

Boosts student model performance on benchmarks.

Achieves higher distillation efficiency.

No significant additional computational overhead.

Abstract

Knowledge Distillation (KD) transfers the knowledge from a high-capacity teacher network to strengthen a smaller student. Existing methods focus on excavating the knowledge hints and transferring the whole knowledge to the student. However, the knowledge redundancy arises since the knowledge shows different values to the student at different learning stages. In this paper, we propose Knowledge Condensation Distillation (KCD). Specifically, the knowledge value on each sample is dynamically estimated, based on which an Expectation-Maximization (EM) framework is forged to iteratively condense a compact knowledge set from the teacher to guide the student learning. Our approach is easy to build on top of the off-the-shelf KD methods, with no extra training parameters and negligible computation overhead. Thus, it presents one new perspective for KD, in which the student that actively identifies teacher's knowledge in line with its aptitude can learn to learn more effectively and efficiently. Experiments on standard benchmarks manifest that the proposed KCD can well boost the performance of student model with even higher distillation efficiency. Code is available at https://github.com/dzy3/KCD.