Entropy minimization (EM) is frequently used to increase the accuracy of
classification models when they're faced with new data at test time. EM is a
self-supervised learning method that optimizes classifiers to assign even
higher probabilities to their top predicted classes. In this paper, we analyze
why EM works when adapting a model for a few steps and why it eventually fails
after adapting for many steps. We show that, at first, EM causes the model to
embed test images close to training images, thereby increasing model accuracy.
After many steps of optimization, EM makes the model embed test images far away
from the embeddings of training images, which results in a degradation of
accuracy. Building upon our insights, we present a method for solving a
practical problem: estimating a model's accuracy on a given arbitrary dataset
without having access to its labels. Our method estimates accuracy by looking
at how the embeddings of input images change as the model is optimized to
minimize entropy. Experiments on 23 challenging datasets show that our method
sets the SoTA with a mean absolute error of $5.75\%$, an improvement of
$29.62\%$ over the previous SoTA on this task. Our code is available at
this https URL

通过分析熵最小化方法在模型适应和优化过程中的作用和原因，本文提出了一个估计模型在给定任意数据集上准确性的方法，并在 23 个具有挑战性的数据集上进行了实验证明其效果在这个任务上达到了最好的状态，绝对误差平均为 5.75%，较先前最好结果提高了 29.62%。