用新指标OUI自动判断模型过拟合或欠拟合，无需验证集调正则化。
OUI Need to Talk About Weight Decay: A New Perspective on Overfitting Detection
- 基于训练过程中的动态变化，提出OUI指标判断模型状态。
- 保持OUI在设定区间内可提升泛化性能，且准确率更高。
- 可在训练早期快速识别最优权重衰减值，适合调参新手和高效实验。
我们提出过拟合-欠拟合指示器（OUI），一种用于监测深度神经网络训练动态并识别最优正则化超参数的新工具。通过在DenseNet-BC-100与CIFAR-100、EfficientNet-B0与TinyImageNet、ResNet-34与ImageNet-1K上的实验，我们验证了将OUI维持在预定区间能显著提升泛化性能与验证分数。值得注意的是，OUI收敛速度远快于传统损失或准确率指标，使从业者能在训练初期即确定最优权重衰减（WD）值。利用OUI作为可靠指标，可早期判断当前WD是否导致欠拟合、过拟合或达到最佳平衡，从而实现对测试数据集与模型的更精准调优。所有实验代码已在GitHub公开：https://github.com/AlbertoFdezHdez/OUI。
原文摘要 · Abstract (English)
We introduce the Overfitting-Underfitting Indicator (OUI), a novel tool for monitoring the training dynamics of Deep Neural Networks (DNNs) and identifying optimal regularization hyperparameters. Specifically, we validate that OUI can effectively guide the selection of the Weight Decay (WD) hyperparameter by indicating whether a model is overfitting or underfitting during training without requiring validation data. Through experiments on DenseNet-BC-100 with CIFAR- 100, EfficientNet-B0 with TinyImageNet and ResNet-34 with ImageNet-1K, we show that maintaining OUI within a prescribed interval correlates strongly with improved generalization and validation scores. Notably, OUI converges significantly faster than traditional metrics such as loss or accuracy, enabling practitioners to identify optimal WD (hyperparameter) values within the early stages of training. By leveraging OUI as a reliable indicator, we can determine early in training whether the chosen WD value leads the model to underfit the training data, overfit, or strike a well-balanced trade-off that maximizes validation scores. This enables more precise WD tuning for optimal performance on the tested datasets and DNNs. All code for reproducing these experiments is available at https://github.com/AlbertoFdezHdez/OUI.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。