  Knowledge distillation refers to a technique of transferring the knowledge
from a large learned model or an ensemble of learned models to a small model.
This method relies on access to the original training set, which might not
always be available. A possible solution is a data-free adversarial
distillation framework, which deploys a generative network to transfer the
teacher model's knowledge to the student model. However, the data generation
efficiency is low in the data-free adversarial distillation. We add an
activation regularizer and a virtual interpolation method to improve the data
generation efficiency. The activation regularizer enables the students to match
the teacher's predictions close to activation boundaries and decision
boundaries. The virtual interpolation method can generate virtual samples and
labels in-between decision boundaries. Our experiments show that our approach
surpasses state-of-the-art data-free distillation methods. The student model
can achieve 95.42% accuracy on CIFAR-10 and 77.05% accuracy on CIFAR-100
without any original training data. Our model's accuracy is 13.8% higher than
the state-of-the-art data-free method on CIFAR-100.
