Data augmentation (DA) is widely employed to improve the generalization performance of deep models. However, most existing DA methods use augmentation operations with random magnitudes throughout training. While this fosters diversity, it can also inevitably introduce uncontrolled variability in augmented data, which may cause misalignment with the evolving training status of the target models. Both theoretical and empirical findings suggest that this misalignment increases the risks of underfitting and overfitting. To address these limitations, we propose AdaAugment, an innovative and tuning-free Adaptive Augmentation method that utilizes reinforcement learning to dynamically adjust augmentation magnitudes for individual training samples based on real-time feedback from the target network. Specifically, AdaAugment features a dual-model architecture consisting of a policy network and a target network, which are jointly optimized to effectively adapt augmentation magnitudes. The policy network optimizes the variability within the augmented data, while the target network utilizes the adaptively augmented samples for training. Extensive experiments across benchmark datasets and deep architectures demonstrate that AdaAugment consistently outperforms other state-of-the-art DA methods in effectiveness while maintaining remarkable efficiency.

翻译：数据增强（DA）被广泛用于提升深度模型的泛化性能。然而，现有的大多数DA方法在整个训练过程中均使用随机强度的增强操作。虽然这有助于提升数据多样性，但也不可避免地会在增强数据中引入不可控的变异性，这可能使其与目标模型不断变化的训练状态产生错配。理论与实证研究均表明，这种错配会增加欠拟合与过拟合的风险。为解决这些局限性，我们提出了AdaAugment，一种创新且无需调优的自适应增强方法。该方法利用强化学习，根据目标网络的实时反馈，动态调整每个训练样本的增强强度。具体而言，AdaAugment采用一种双模型架构，包含一个策略网络和一个目标网络，二者协同优化以有效适配增强强度。策略网络负责优化增强数据内部的变异性，而目标网络则利用这些自适应增强的样本进行训练。在多个基准数据集和深度架构上进行的大量实验表明，AdaAugment在保持显著效率的同时，其有效性持续优于其他最先进的DA方法。