On the Efficacy of Knowledge Distillation
This addresses a key bottleneck in model compression for deployment, though it is incremental as it builds on existing distillation methods.
The paper investigates knowledge distillation, finding that larger or more accurate teachers often do not improve student performance due to mismatched capacity, and shows that stopping teacher training early can mitigate this issue, with results generalizing across datasets and models.
In this paper, we present a thorough evaluation of the efficacy of knowledge distillation and its dependence on student and teacher architectures. Starting with the observation that more accurate teachers often don't make good teachers, we attempt to tease apart the factors that affect knowledge distillation performance. We find crucially that larger models do not often make better teachers. We show that this is a consequence of mismatched capacity, and that small students are unable to mimic large teachers. We find typical ways of circumventing this (such as performing a sequence of knowledge distillation steps) to be ineffective. Finally, we show that this effect can be mitigated by stopping the teacher's training early. Our results generalize across datasets and models.