"Accuracy-on-the-line" is a widely observed phenomenon in machine learning,
where a model's accuracy on in-distribution (ID) and out-of-distribution (OOD)
data is positively correlated across different hyperparameters and data
configurations. But when does this useful relationship break down? In this
work, we explore its robustness. The key observation is that noisy data and the
presence of nuisance features can be sufficient to shatter the
Accuracy-on-the-line phenomenon. In these cases, ID and OOD accuracy can become
negatively correlated, leading to "Accuracy-on-the-wrong-line". This phenomenon
can also occur in the presence of spurious (shortcut) features, which tend to
overshadow the more complex signal (core, non-spurious) features, resulting in
a large nuisance feature space. Moreover, scaling to larger datasets does not
mitigate this undesirable behavior and may even exacerbate it. We formally
prove a lower bound on Out-of-distribution (OOD) error in a linear
classification model, characterizing the conditions on the noise and nuisance
features for a large OOD error. We finally demonstrate this phenomenon across
both synthetic and real datasets with noisy data and nuisance features.

通过研究机器学习中的 “在线准确性” 现象，我们发现噪声数据和干扰特征会破坏准确性与超参数和数据配置之间的正相关关系，导致准确性与超参数和数据配置之间出现负相关关系。这种现象也可能出现在含有假特征的情况下，假特征会凸显出复杂信号特征，导致大量干扰特征空间。此外，将数据扩展到更大规模并不能减轻这种不良行为，甚至可能加剧它。我们通过线性分类模型在真实和合成数据集上进行验证，并对噪声数据和干扰特征的条件下的 Out-of-distribution (OOD) 错误证明了下界。

错误数据上的准确度：关于嘈杂数据对于超出分布泛化的陷阱

Accuracy on the wrong line: On the pitfalls of noisy data for  out-of-distribution generalisation

Recently, Miller et al. showed that a model's in-distribution (ID) accuracy
has a strong linear correlation with its out-of-distribution (OOD) accuracy on
several OOD benchmarks -- a phenomenon they dubbed ''accuracy-on-the-line''.
While a useful tool for model selection (i.e., the model most likely to perform
the best OOD is the one with highest ID accuracy), this fact does not help
estimate the actual OOD performance of models without access to a labeled OOD
validation set. In this paper, we show a similar but surprising phenomenon also
holds for the agreement between pairs of neural network classifiers: whenever
accuracy-on-the-line holds, we observe that the OOD agreement between the
predictions of any two pairs of neural networks (with potentially different
architectures) also observes a strong linear correlation with their ID
agreement. Furthermore, we observe that the slope and bias of OOD vs ID
agreement closely matches that of OOD vs ID accuracy. This phenomenon, which we
call agreement-on-the-line, has important practical applications: without any
labeled data, we can predict the OOD accuracy of classifiers}, since OOD
agreement can be estimated with just unlabeled data. Our prediction algorithm
outperforms previous methods both in shifts where agreement-on-the-line holds
and, surprisingly, when accuracy is not on the line. This phenomenon also
provides new insights into deep neural networks: unlike accuracy-on-the-line,
agreement-on-the-line appears to only hold for neural network classifiers.

本文研究了神经网络分类器之间的一致性现象以及其与准确性的线性关系，提出了一种只利用无标签数据进行 ODA 预测的算法，并探讨了这种现象对于深度神经网络的新见解。