Adversarial Training in Low-Label Regimes With Margin-Based Interpolation

Deep neural networks can be fooled by adversarial attacks (small, carefully crafted changes to input data that cause incorrect predictions), but training them to resist these attacks usually requires large amounts of labeled data. This paper proposes margin-based interpolation, a technique that adjusts how strongly to attack training data based on each example's difficulty and reliability, and uses global epsilon scheduling (gradually increasing perturbation strength during training) to help models become robust while maintaining accuracy, even with limited labeled data.