Knowledge Distillation via Route Constrained Optimization
This work addresses the challenge of efficiently training small neural networks via knowledge distillation, offering a method to enhance model compression for applications like image classification and face recognition.
The paper tackles the problem of knowledge distillation by proposing route constrained optimization (RCO), which uses anchor points from a teacher model's training route instead of its converged state, reducing congruence loss and improving performance on classification tasks like CIFAR100 and ImageNet by 2.14% and 1.5%, respectively.
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.