Reliable application of machine learning is of primary importance to the
practical deployment of deep learning methods. A fundamental challenge is that
models are often unreliable due to overconfidence. In this paper, we estimate a
model's reliability by measuring \emph{the agreement between its latent space,
and the latent space of a foundation model}. However, it is challenging to
measure the agreement between two different latent spaces due to their
incoherence, \eg, arbitrary rotations and different dimensionality. To overcome
this incoherence issue, we design a \emph{neighborhood agreement measure}
between latent spaces and find that this agreement is surprisingly
well-correlated with the reliability of a model's predictions. Further, we show
that fusing neighborhood agreement into a model's predictive confidence in a
post-hoc way significantly improves its reliability. Theoretical analysis and
extensive experiments on failure detection across various datasets verify the
effectiveness of our method on both in-distribution and out-of-distribution
settings.

本文提出了一种通过测量模型潜在空间与基础模型潜在空间之间的一致性来估计模型可靠性的方法，并设计了一种邻域一致性度量方法，该方法与模型的预测置信度相结合显著提高了模型的可靠性，并在各种数据集上的失败检测实验中验证了该方法的有效性。