自适应标签平滑在多种数据增强下效果变差，仅在单一增强类型时有效。
Combined Image Data Augmentations diminish the benefits of Adaptive Label Smoothing
- 将标签平滑动态调整为随增强强度变化，提升正则化效果。
- 在混合多种增强时，自适应平滑的收益消失，且降低对常见损坏的鲁棒性。
- 适合仅使用有限、同质增强策略的场景，如随机裁剪或单一噪声注入。
软增强通过根据随机裁剪强度降低训练样本的标签置信度，来正则化图像分类器的监督学习过程。本文将这一自适应标签平滑框架扩展至随机擦除和噪声注入等其他强增强方法。实验表明，该方法在随机擦除中表现良好，可支持更高强度的增强；但当与TrivialAugment这类包含多样化图像变换的方法结合时，其优势消失，过度平滑反而损害模型对常见图像损坏的鲁棒性。研究结果提示，自适应标签平滑仅应在训练数据分布主要由少数同质增强类型主导时使用。
原文摘要 · Abstract (English)
Soft augmentation regularizes the supervised learning process of image classifiers by reducing label confidence of a training sample based on the magnitude of random-crop augmentation applied to it. This paper extends this adaptive label smoothing framework to other types of aggressive augmentations beyond random-crop. Specifically, we demonstrate the effectiveness of the method for random erasing and noise injection data augmentation. Adaptive label smoothing permits stronger regularization via higher-intensity Random Erasing. However, its benefits vanish when applied with a diverse range of image transformations as in the state-of-the-art TrivialAugment method, and excessive label smoothing harms robustness to common corruptions. Our findings suggest that adaptive label smoothing should only be applied when the training data distribution is dominated by a limited, homogeneous set of image transformation types.
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