用10%噪声数据训练，让CNN在干扰下更稳更准
The Role of Noisy Data in Improving CNN Robustness for Image Classification
- 在训练中加入可控噪声，模拟真实世界干扰
- 仅10%噪声数据使测试损失下降，恶劣条件下准确率提升
- 适合追求实际应用鲁棒性的模型开发者
数据质量对卷积神经网络（CNN）图像分类的性能与鲁棒性至关重要。尽管高质量数据通常被优先选用，但真实输入常受噪声与失真影响。本文研究有意识地在训练数据中引入可控噪声以提升模型鲁棒性。基于CIFAR-10数据集，评估了高斯噪声、盐椒噪声和高斯模糊三种常见退化在不同强度与训练集污染水平下的影响。使用ResNet-18模型的实验表明，仅在训练中引入10%噪声数据，即可显著降低测试损失，并在完全退化测试条件下提升准确率，对干净数据表现影响极小。结果表明，策略性暴露于噪声可作为简单而有效的正则化手段，在传统数据纯净性与真实世界抗扰性之间提供实用平衡。
原文摘要 · Abstract (English)
Data quality plays a central role in the performance and robustness of convolutional neural networks (CNNs) for image classification. While high-quality data is often preferred for training, real-world inputs are frequently affected by noise and other distortions. This paper investigates the effect of deliberately introducing controlled noise into the training data to improve model robustness. Using the CIFAR-10 dataset, we evaluate the impact of three common corruptions, namely Gaussian noise, Salt-and-Pepper noise, and Gaussian blur at varying intensities and training set pollution levels. Experiments using a Resnet-18 model reveal that incorporating just 10\% noisy data during training is sufficient to significantly reduce test loss and enhance accuracy under fully corrupted test conditions, with minimal impact on clean-data performance. These findings suggest that strategic exposure to noise can act as a simple yet effective regularizer, offering a practical trade-off between traditional data cleanliness and real-world resilience.
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