标题： 在虚假相关性存在的情况下揭示记忆效应 显示英文标题

标题： Uncovering Memorization Effect in the Presence of Spurious Correlations

摘要： 机器学习模型常常依赖于简单的虚假特征——即训练数据中与目标相关联但与其无因果关系的模式，比如在前景分类中的图像背景。 这种依赖性通常会导致少数群体和多数群体之间的测试性能不平衡。 在这项工作中，我们通过记忆的视角更深入地研究了这种性能不平衡的根本原因，记忆指的是在训练集中准确预测非典型样本（少数群体）的能力，但在测试集中却无法达到相同的准确性。 本文系统地展示了网络中小部分神经元内普遍存在虚假特征，并提供了首个证据表明记忆可能有助于解释群体间性能的不平衡。 通过三个实验来源的汇聚实证证据，我们发现小部分神经元或通道具有记忆少数群体信息的特性。 受这些发现启发，我们假设虚假记忆，集中在少量神经元内，是导致群体间性能不平衡的关键因素。 为进一步验证这一假设，我们展示了通过一种新颖框架在训练过程中消除这些不必要的虚假记忆模式，可以显著影响模型在少数群体上的表现。 我们的实验结果涵盖多种架构和基准，为理解神经网络如何编码核心与虚假知识提供了新的见解，为未来研究如何破解虚假相关性的鲁棒性奠定了基础。 显示英文摘要

摘要： Machine learning models often rely on simple spurious features -- patterns in training data that correlate with targets but are not causally related to them, like image backgrounds in foreground classification. This reliance typically leads to imbalanced test performance across minority and majority groups. In this work, we take a closer look at the fundamental cause of such imbalanced performance through the lens of memorization, which refers to the ability to predict accurately on atypical examples (minority groups) in the training set but failing in achieving the same accuracy in the testing set. This paper systematically shows the ubiquitous existence of spurious features in a small set of neurons within the network, providing the first-ever evidence that memorization may contribute to imbalanced group performance. Through three experimental sources of converging empirical evidence, we find the property of a small subset of neurons or channels in memorizing minority group information. Inspired by these findings, we hypothesize that spurious memorization, concentrated within a small subset of neurons, plays a key role in driving imbalanced group performance. To further substantiate this hypothesis, we show that eliminating these unnecessary spurious memorization patterns via a novel framework during training can significantly affect the model performance on minority groups. Our experimental results across various architectures and benchmarks offer new insights on how neural networks encode core and spurious knowledge, laying the groundwork for future research in demystifying robustness to spurious correlation.