  We propose a novel model-centric evaluation framework, OmniInput, to evaluate
the quality of an AI/ML model's predictions on all possible inputs (including
human-unrecognizable ones), which is crucial for AI safety and reliability.
Unlike traditional data-centric evaluation based on pre-defined test sets, the
test set in OmniInput is self-constructed by the model itself and the model
quality is evaluated by investigating its output distribution. We employ an
efficient sampler to obtain representative inputs and the output distribution
of the trained model, which, after selective annotation, can be used to
estimate the model's precision and recall at different output values and a
comprehensive precision-recall curve. Our experiments demonstrate that
OmniInput enables a more fine-grained comparison between models, especially
when their performance is almost the same on pre-defined datasets, leading to
new findings and insights for how to train more robust, generalizable models.
