Knowledge Distillation as Semiparametric Inference
This work addresses the challenge of enhancing knowledge distillation efficiency for practitioners in machine learning, though it is incremental as it builds on existing distillation methods with theoretical refinements.
The paper tackles the problem of explaining and improving knowledge distillation for model compression by framing it as a semiparametric inference problem, resulting in new theoretical guarantees and two enhancements (cross-fitting and loss correction) that consistently improve student performance on tabular and image data.
A popular approach to model compression is to train an inexpensive student model to mimic the class probabilities of a highly accurate but cumbersome teacher model. Surprisingly, this two-step knowledge distillation process often leads to higher accuracy than training the student directly on labeled data. To explain and enhance this phenomenon, we cast knowledge distillation as a semiparametric inference problem with the optimal student model as the target, the unknown Bayes class probabilities as nuisance, and the teacher probabilities as a plug-in nuisance estimate. By adapting modern semiparametric tools, we derive new guarantees for the prediction error of standard distillation and develop two enhancements -- cross-fitting and loss correction -- to mitigate the impact of teacher overfitting and underfitting on student performance. We validate our findings empirically on both tabular and image data and observe consistent improvements from our knowledge distillation enhancements.