Document Type
Article
Publication Date
8-20-2024
Publication Title
Empirical Software Engineering
Volume
29
Pages
1 - 61
Publisher Name
Springer
Abstract
Many engineering organizations are reimplementing and extending deep neural networks from the research community. We describe this process as deep learning model reengineering. Deep learning model reengineering — reusing, replicating, adapting, and enhancing state-of-the-art deep learning approaches — is challenging for reasons including under-documented reference models, changing requirements, and the cost of implementation and testing.
Recommended Citation
Jiang, W., V. Banna, N. Vivek, A. Goel, N. Synovic, G. K. Thiruvathukal, and J. C. Davis. "Challenges and Practices of Deep Learning Model Reengineering: A Case Study on Computer Vision." Empirical Software Engineering, vol. 29, no. 142, 2024, https://doi.org/10.1007/s10664-024-10521-0.
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 International License.
Copyright Statement
© The Author(s), 2024
Comments
Author Posting © The Author(s), 2024. This article is posted here by permission of Springer for personal use and redistribution. This article was published open access in Empirical Software Engineering, VOL.29, (August 20, 2024),https://doi.org/10.1007/s10664-024-10521-0.