Data-free compression raises a new challenge because the original training
dataset for a pre-trained model to be compressed is not available due to
privacy or transmission issues. Thus, a common approach is to compute a
reconstructed training dataset before compression. The current reconstruction
methods compute the reconstructed training dataset with a generator by
exploiting information from the pre-trained model. However, current
reconstruction methods focus on extracting more information from the
pre-trained model but do not leverage network engineering. This work is the
first to consider network engineering as an approach to design the
reconstruction method. Specifically, we propose the AutoReCon method, which is
a neural architecture search-based reconstruction method. In the proposed
AutoReCon method, the generator architecture is designed automatically given
the pre-trained model for reconstruction. Experimental results show that using
generators discovered by the AutoRecon method always improve the performance of
data-free compression.

本研究提出了 AutoReCon 方法，这是一种基于神经架构搜索的重构方法，通过自动设计生成器结构以实现对预先训练模型重构的效果达到提高数据无关压缩的方法。