  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.
