Labeling-Free Comparison Testing of Deep Learning Models. (arXiv:2204.03994v1 [cs.LG])

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 …

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