Publication Type

Journal Article

Version

publishedVersion

Publication Date

11-2023

Abstract

pplying deep learning (DL) to science is a new trend in recent years, which leads DL engineering to become an important problem. Although training data preparation, model architecture design, and model training are the normal processes to build DL models, all of them are complex and costly. Therefore, reusing the open-sourced pre-trained model is a practical way to bypass this hurdle for developers. Given a specific task, developers can collect massive pre-trained deep neural networks from public sources for reusing. However, testing the performance (e.g., accuracy and robustness) of multiple deep neural networks (DNNs) and recommending which model should be used is challenging regarding the scarcity of labeled data and the demand for domain expertise. In this article, we propose a labeling-free (LaF) model selection approach to overcome the limitations of labeling efforts for automated model reusing. The main idea is to statistically learn a Bayesian model to infer the models’ specialty only based on predicted labels. We evaluate LaF using nine benchmark datasets, including image, text, and source code, and 165 DNNs, considering both the accuracy and robustness of models. The experimental results demonstrate that LaF outperforms the baseline methods by up to 0.74 and 0.53 on Spearman’s correlation and Kendall’s τ, respectively.

Keywords

Computing methodologies, Artificial intelligence, Software and its engineering, Software creation and management, Software development process management

Discipline

Databases and Information Systems

Research Areas

Data Science and Engineering; Cybersecurity

Publication

ACM Transactions on Software Engineering and Methodology

Volume

33

Issue

1

First Page

1

Last Page

28

ISSN

1049-331X

Identifier

10.1145/3611666

Publisher

Association for Computing Machinery (ACM)

Copyright Owner and License

Authors

Additional URL

https://doi.org/10.1145/3611666

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