@inproceedings{
xie2024mano,
title={MaNo: Exploiting Matrix Norm for Unsupervised Accuracy Estimation Under Distribution Shifts},
author={RENCHUNZI XIE and Ambroise Odonnat and Vasilii Feofanov and Weijian Deng and Jianfeng Zhang and Bo An},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=mH1xtt2bJE}
}
Leveraging the model’s 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 \method{} that \textbf{(1)}~applies a data-dependent normalization on the logits to reduce prediction bias, and \textbf{(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 \method{} achieves state-of-the-art performance across various architectures in the presence of synthetic, natural, or subpopulation shifts. The code is available at https://github.com/Renchunzi-Xie/MaNo.
MaNo: Exploiting Matrix Norm for Unsupervised Accuracy Estimation Under Distribution Shifts
RENCHUNZI XIE Ambroise Odonnat Vasilii Feofanov Weijian Deng Jianfeng Zhang Bo An