模型记住了异常数据，反而学不会通用规律，新方法让模型更健壮。
The Pitfalls of Memorization: When Memorization Hurts Generalization
- 用保留预测信号检测记忆行为，动态调整模型输出
- 在分布变化下准确率提升12.3%，避免因虚假关联导致错误
- 适合需要跨数据集泛化的场景，如医疗诊断、自动驾驶
神经网络常学习多数数据的简单解释，同时记忆偏离这些解释的例外情况。这种行为会导致模型在依赖虚假相关性时泛化性能下降。本文形式化了记忆与泛化之间的相互作用，表明当虚假相关性与记忆结合时，泛化性能会显著恶化。记忆可使训练损失降至零，从而削弱模型学习鲁棒、可泛化模式的动力。为此，我们提出记忆感知训练（MAT），利用保留样本的预测结果作为记忆信号，对模型输出进行调整。MAT促使模型学习跨分布不变的稳健模式，在分布偏移下显著提升泛化能力。
原文摘要 · Abstract (English)
Neural networks often learn simple explanations that fit the majority of the data while memorizing exceptions that deviate from these explanations.This behavior leads to poor generalization when the learned explanations rely on spurious correlations. In this work, we formalize the interplay between memorization and generalization, showing that spurious correlations would particularly lead to poor generalization when are combined with memorization. Memorization can reduce training loss to zero, leaving no incentive to learn robust, generalizable patterns. To address this, we propose memorization-aware training (MAT), which uses held-out predictions as a signal of memorization to shift a model's logits. MAT encourages learning robust patterns invariant across distributions, improving generalization under distribution shifts.
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