  With the increasing popularity of deep learning on edge devices, compressing
large neural networks to meet the hardware requirements of resource-constrained
devices became a significant research direction. Numerous compression
methodologies are currently being used to reduce the memory sizes and energy
consumption of neural networks. Knowledge distillation (KD) is among such
methodologies and it functions by using data samples to transfer the knowledge
captured by a large model (teacher) to a smaller one(student). However, due to
various reasons, the original training data might not be accessible at the
compression stage. Therefore, data-free model compression is an ongoing
research problem that has been addressed by various works. In this paper, we
point out that catastrophic forgetting is a problem that can potentially be
observed in existing data-free distillation methods. Moreover, the sample
generation strategies in some of these methods could result in a mismatch
between the synthetic and real data distributions. To prevent such problems, we
propose a data-free KD framework that maintains a dynamic collection of
generated samples over time. Additionally, we add the constraint of matching
the real data distribution in sample generation strategies that target maximum
information gain. Our experiments demonstrate that we can improve the accuracy
of the student models obtained via KD when compared with state-of-the-art
approaches on the SVHN, Fashion MNIST and CIFAR100 datasets.
