  This paper evaluates the generalization ability of classification models on
out-of-distribution test sets without depending on ground truth labels. Common
approaches often calculate an unsupervised metric related to a specific model
property, like confidence or invariance, which correlates with
out-of-distribution accuracy. However, these metrics are typically computed for
each test sample individually, leading to potential issues caused by spurious
model responses, such as overly high or low confidence. To tackle this
challenge, we propose incorporating responses from neighboring test samples
into the correctness assessment of each individual sample. In essence, if a
model consistently demonstrates high correctness scores for nearby samples, it
increases the likelihood of correctly predicting the target sample, and vice
versa. The resulting scores are then averaged across all test samples to
provide a holistic indication of model accuracy. Developed under the vicinal
risk formulation, this approach, named vicinal risk proxy (VRP), computes
accuracy without relying on labels. We show that applying the VRP method to
existing generalization indicators, such as average confidence and effective
invariance, consistently improves over these baselines both methodologically
and experimentally. This yields a stronger correlation with model accuracy,
especially on challenging out-of-distribution test sets.
