Adversarial local distribution regularization for knowledge distillation

Knowledge distillation is a process of distilling information from a large model with significant knowledge capacity (teacher) to enhance a smaller model (student). Therefore, exploring the properties of the teacher is the key to improving student performance (e.g., teacher decision boundaries). One decision boundary exploring technique is to leverage adversarial attack methods, which add crafted perturbations within a ball constraint to clean inputs to create attack examples of the teacher called adversarial examples. These adversarial examples are informative examples because they are near decision boundaries. In this paper, we formulate a teacher adversarial local distribution, a set of all adversarial examples within the ball constraint given an input. This distribution is used to sufficiently explore the decision boundaries of the teacher by covering the full spectrum of possible teacher model perturbations. The student model is then regularized by matching the loss between teacher and student using these adversarial example inputs. We conducted a number of experiments on CIFAR-100 and Imagenet datasets to illustrate this teacher adversarial local distribution regularization (TALD) can be applied to improve performance of many existing knowledge distillation methods (e.g., KD, FitNet, CRD, VID, FT, etc.).