  In the last decade, many deep learning models have been well trained and made
a great success in various fields of machine intelligence, especially for
computer vision and natural language processing. To better leverage the
potential of these well-trained models in intra-domain or cross-domain transfer
learning situations, knowledge distillation (KD) and domain adaptation (DA) are
proposed and become research highlights. They both aim to transfer useful
information from a well-trained model with original training data. However, the
original data is not always available in many cases due to privacy, copyright
or confidentiality. Recently, the data-free knowledge transfer paradigm has
attracted appealing attention as it deals with distilling valuable knowledge
from well-trained models without requiring to access to the training data. In
particular, it mainly consists of the data-free knowledge distillation (DFKD)
and source data-free domain adaptation (SFDA). On the one hand, DFKD aims to
transfer the intra-domain knowledge of original data from a cumbersome teacher
network to a compact student network for model compression and efficient
inference. On the other hand, the goal of SFDA is to reuse the cross-domain
knowledge stored in a well-trained source model and adapt it to a target
domain. In this paper, we provide a comprehensive survey on data-free knowledge
transfer from the perspectives of knowledge distillation and unsupervised
domain adaptation, to help readers have a better understanding of the current
research status and ideas. Applications and challenges of the two areas are
briefly reviewed, respectively. Furthermore, we provide some insights to the
subject of future research.
