Boosting Model Resilience via Implicit Adversarial Data Augmentation

arXiv:2404.16307v1 Announce Type: new
Abstract: 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 …

abstract adversarial arxiv augment augmentation biases boosting cs.cv cs.lg data features improving performance pivotal resilience role samples training training data type via