  Recently, the compression and deployment of powerful deep neural networks
(DNNs) on resource-limited edge devices to provide intelligent services have
become attractive tasks. Although knowledge distillation (KD) is a feasible
solution for compression, its requirement on the original dataset raises
privacy concerns. In addition, it is common to integrate multiple pretrained
models to achieve satisfactory performance. How to compress multiple models
into a tiny model is challenging, especially when the original data are
unavailable. To tackle this challenge, we propose a framework termed
collaborative data-free knowledge distillation via multi-level feature sharing
(CDFKD-MFS), which consists of a multi-header student module, an asymmetric
adversarial data-free KD module, and an attention-based aggregation module. In
this framework, the student model equipped with a multi-level feature-sharing
structure learns from multiple teacher models and is trained together with a
generator in an asymmetric adversarial manner. When some real samples are
available, the attention module adaptively aggregates predictions of the
student headers, which can further improve performance. We conduct extensive
experiments on three popular computer visual datasets. In particular, compared
with the most competitive alternative, the accuracy of the proposed framework
is 1.18\% higher on the CIFAR-100 dataset, 1.67\% higher on the Caltech-101
dataset, and 2.99\% higher on the mini-ImageNet dataset.
