Knowledge Distillation via Route Constrained Optimization

TL;DR

This paper introduces route constrained optimization (RCO), a novel knowledge distillation method inspired by curriculum learning, which improves the training of small neural networks by using selected route points from the teacher model's parameter space.

Contribution

The paper proposes RCO, a new approach that reduces the congruence loss in knowledge distillation by routing through parameter space, enhancing performance on classification and face recognition tasks.

Findings

RCO improves accuracy on CIFAR100 by 2.14%.

RCO enhances ImageNet performance by 1.5%.

RCO demonstrates better generalization on MegaFace face recognition.

Abstract

Distillation-based learning boosts the performance of the miniaturized neural network based on the hypothesis that the representation of a teacher model can be used as structured and relatively weak supervision, and thus would be easily learned by a miniaturized model. However, we find that the representation of a converged heavy model is still a strong constraint for training a small student model, which leads to a high lower bound of congruence loss. In this work, inspired by curriculum learning we consider the knowledge distillation from the perspective of curriculum learning by routing. Instead of supervising the student model with a converged teacher model, we supervised it with some anchor points selected from the route in parameter space that the teacher model passed by, as we called route constrained optimization (RCO). We experimentally demonstrate this simple operation greatly reduces the lower bound of congruence loss for knowledge distillation, hint and mimicking learning. On close-set classification tasks like CIFAR100 and ImageNet, RCO improves knowledge distillation by 2.14% and 1.5% respectively. For the sake of evaluating the generalization, we also test RCO on the open-set face recognition task MegaFace.