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AutoReCon: Neural Architecture Search-based Reconstruction for Data-free Compression

Cited 0 time in Web of Science Cited 6 time in Scopus
Authors

Zhu, Baozhou; Hofstee, Peter; Peltenburg, Johan; Lee, Jinho; Alars, Zaid

Issue Date
2021
Publisher
International Joint Conferences on Artificial Intelligence
Citation
IJCAI International Joint Conference on Artificial Intelligence, pp.3470-3476
Abstract
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.
ISSN
1045-0823
URI
https://hdl.handle.net/10371/200474
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  • College of Engineering
  • Department of Electrical and Computer Engineering
Research Area AI Accelerators, Distributed Deep Learning, Neural Architecture Search

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