Supervision complexity and its role in knowledge distillation

Knowledge distillation is a popular method of compressing a large teacher model (or an ensemble of models) to a more compact student model. While empirically effective, there is limited understanding of why distillation helps, and how to improve it to transfer richer knowledge from the teacher to student. In this paper, we propose a new online distillation algorithm that applies distillation using a sequence of teacher models, corresponding to different checkpoints during teacher training. Intuitively, this gradually increases the complexity of the target functions that the student model is asked to mimic. Formally, we establish generalization bounds that explicate how the target label complexity can benefit the student. We empirically demonstrate that online distillation can significantly improve over regular offline distillation, particularly in scenarios where there is a large teacher-student capacity gap.