We propose a novel class incremental learning approach by incorporating a feature augmentation technique motivated by adversarial attacks. We employ a classifier learned in the past to complement training examples rather than simply play a role as a teacher for knowledge distillation towards subsequent models. The proposed approach has a unique perspective to utilize the previous knowledge in class incremental learning since it augments features of arbitrary target classes using examples in other classes via adversarial attacks on a previously learned classifier. By allowing the cross-class feature augmentations, each class in the old tasks conveniently populates samples in the feature space, which alleviates the collapse of the decision boundaries caused by sample deficiency for the previous tasks, especially when the number of stored exemplars is small. This idea can be easily incorporated into existing class incremental learning algorithms without any architecture modification. Extensive experiments on the standard benchmarks show that our method consistently outperforms existing class incremental learning methods by significant margins in various scenarios, especially under an environment with an extremely limited memory budget.

翻译：我们提出了一种新颖的增量类别学习方法，该方法融合了受对抗攻击启发的特征增强技术。本文利用过去学习得到的分类器来补充训练样本，而非仅仅作为知识蒸馏中的教师模型以指导后续模型。该方法的独特之处在于，通过利用先前分类器对样本进行对抗攻击，将其他类别的示例特征增强至任意目标类别，从而在增量学习过程中充分运用先前知识。通过实现跨类别特征增强，旧任务中的每个类别能够在特征空间中便捷地生成样本，这有效缓解了因样本不足导致的决策边界崩塌问题——尤其当存储样本数量较少时。该思想无需修改模型架构即可轻松集成至现有增量类别学习算法。在标准基准上的大量实验表明，本文方法在各场景下均显著优于现有增量类别学习方法，特别是在极端内存限制环境下表现尤为突出。