  Leveraging the models' outputs, specifically the logits, is a common approach
to estimating the test accuracy of a pre-trained neural network on
out-of-distribution (OOD) samples without requiring access to the corresponding
ground truth labels. Despite their ease of implementation and computational
efficiency, current logit-based methods are vulnerable to overconfidence
issues, leading to prediction bias, especially under the natural shift. In this
work, we first study the relationship between logits and generalization
performance from the view of low-density separation assumption. Our findings
motivate our proposed method MaNo which (1) applies a data-dependent
normalization on the logits to reduce prediction bias, and (2) takes the $L_p$
norm of the matrix of normalized logits as the estimation score. Our
theoretical analysis highlights the connection between the provided score and
the model's uncertainty. We conduct an extensive empirical study on common
unsupervised accuracy estimation benchmarks and demonstrate that MaNo achieves
state-of-the-art performance across various architectures in the presence of
synthetic, natural, or subpopulation shifts.
