Data augmentation plays a pivotal role in enhancing and diversifying training data. Nonetheless, consistently improving model performance in varied learning scenarios, especially those with inherent data biases, remains challenging. To address this, we propose to augment the deep features of samples by incorporating their adversarial and anti-adversarial perturbation distributions, enabling adaptive adjustment in the learning difficulty tailored to each sample's specific characteristics. We then theoretically reveal that our augmentation process approximates the optimization of a surrogate loss function as the number of augmented copies increases indefinitely. This insight leads us to develop a meta-learning-based framework for optimizing classifiers with this novel loss, introducing the effects of augmentation while bypassing the explicit augmentation process. We conduct extensive experiments across four common biased learning scenarios: long-tail learning, generalized long-tail learning, noisy label learning, and subpopulation shift learning. The empirical results demonstrate that our method consistently achieves state-of-the-art performance, highlighting its broad adaptability.

通过融入对抗性和反对抗性扰动分布，增强样本的深度特征，适应性调整学习困难以适应每个样本的特征。同时，开发了一种基于元学习的框架，通过引入增强的效果并跳过显式的增强过程，优化分类器。在长尾学习、广义长尾学习、嘈杂标记学习和子种群转移学习等四种常见偏差学习场景中进行了广泛实验，实证结果表明该方法始终达到最先进的性能水平，突显其广泛适应性。

通过隐式对抗数据增强提升模型的弹性