FlyKD: Graph Knowledge Distillation on the Fly with Curriculum Learning
Eugene Ku
Knowledge Distillation (KD) aims to transfer a more capable teacher model's
knowledge to a lighter student model in order to improve the efficiency of the
model, making it faster and more deployable. However, the student model's
optimization process over the noisy pseudo labels (generated by the teacher
model) is tricky and the amount of pseudo labels one can generate is limited
due to Out of Memory (OOM) error. In this paper, we propose FlyKD (Knowledge
Distillation on the Fly) which enables the generation of virtually unlimited
number of pseudo labels, coupled with Curriculum Learning that greatly
alleviates the optimization process over the noisy pseudo labels. Empirically,
we observe that FlyKD outperforms vanilla KD and the renown Local Structure
Preserving Graph Convolutional Network (LSPGCN). Lastly, with the success of
Curriculum Learning, we shed light on a new research direction of improving
optimization over noisy pseudo labels.