  Deep network models perform excellently on In-Distribution (ID) data, but can
significantly fail on Out-Of-Distribution (OOD) data. While developing methods
focus on improving OOD generalization, few attention has been paid to
evaluating the capability of models to handle OOD data. This study is devoted
to analyzing the problem of experimental ID test and designing OOD test
paradigm to accurately evaluate the practical performance. Our analysis is
based on an introduced categorization of three types of distribution shifts to
generate OOD data. Main observations include: (1) ID test fails in neither
reflecting the actual performance of a single model nor comparing between
different models under OOD data. (2) The ID test failure can be ascribed to the
learned marginal and conditional spurious correlations resulted from the
corresponding distribution shifts. Based on this, we propose novel OOD test
paradigms to evaluate the generalization capacity of models to unseen data, and
discuss how to use OOD test results to find bugs of models to guide model
debugging.
