  Applying deep learning 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 re-using. However,
testing the performance (e.g., accuracy and robustness) of multiple DNNs and
recommending which model should be used is challenging regarding the scarcity
of labeled data and the demand for domain expertise. In this paper, 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 9 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 $\tau$,
respectively.
