Labeling-Free Comparison Testing of Deep Learning Models

• URL: http://arxiv.org/abs/2204.03994v1
• Date: Fri, 8 Apr 2022 10:55:45 GMT
• Title: Labeling-Free Comparison Testing of Deep Learning Models
• Authors: Yuejun Guo, Qiang Hu, Maxime Cordy, Xiaofei Xie, Mike Papadakis, Yves Le Traon
• Abstract summary: We propose a labeling-free comparison testing approach to overcome the limitations of labeling effort and sampling randomness. Our approach outperforms the baseline methods by up to 0.74 and 0.53 on Spearman's correlation and Kendall's $tau$, regardless of the dataset and distribution shift.
• Score: 28.47632100019289
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Various deep neural networks (DNNs) are developed and reported for their tremendous success in multiple domains. Given a specific task, developers can collect massive DNNs from public sources for efficient reusing and avoid redundant work from scratch. However, testing the performance (e.g., accuracy and robustness) of multiple DNNs and giving a reasonable recommendation that which model should be used is challenging regarding the scarcity of labeled data and demand of domain expertise. Existing testing approaches are mainly selection-based where after sampling, a few of the test data are labeled to discriminate DNNs. Therefore, due to the randomness of sampling, the performance ranking is not deterministic. In this paper, we propose a labeling-free comparison testing approach to overcome the limitations of labeling effort and sampling randomness. The main idea is to learn a Bayesian model to infer the models' specialty only based on predicted labels. To evaluate the effectiveness of our approach, we undertook exhaustive experiments on 9 benchmark datasets spanning in the domains of image, text, and source code, and 165 DNNs. In addition to accuracy, we consider the robustness against synthetic and natural distribution shifts. The experimental results demonstrate that the performance of existing approaches degrades under distribution shifts. Our approach outperforms the baseline methods by up to 0.74 and 0.53 on Spearman's correlation and Kendall's $\tau$, respectively, regardless of the dataset and distribution shift. Additionally, we investigated the impact of model quality (accuracy and robustness) and diversity (standard deviation of the quality) on the testing effectiveness and observe that there is a higher chance of a good result when the quality is over 50\% and the diversity is larger than 18\%.

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