Knowledge Condensation Distillation
This work addresses knowledge redundancy in knowledge distillation for improving model compression and efficiency, though it is incremental as it builds on existing KD methods.
The paper tackles the problem of knowledge redundancy in knowledge distillation by proposing Knowledge Condensation Distillation (KCD), which dynamically estimates knowledge value and uses an EM framework to condense a compact knowledge set, resulting in boosted student performance and higher distillation efficiency on standard benchmarks.
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.