Rehearsal approaches in class incremental learning (CIL) suffer from decision boundary overfitting to new classes, which is mainly caused by two factors: insufficiency of old classes data for knowledge distillation and imbalanced data learning between the learned and new classes because of the limited storage memory. In this work, we present a simple but effective approach to tackle these two factors. First, we employ a re-sampling strategy and Mixup K}nowledge D}istillation (Re-MKD) to improve the performances of KD, which would greatly alleviate the overfitting problem. Specifically, we combine mixup and re-sampling strategies to synthesize adequate data used in KD training that are more consistent with the latent distribution between the learned and new classes. Second, we propose a novel incremental influence balance (IIB) method for CIL to tackle the classification of imbalanced data by extending the influence balance method into the CIL setting, which re-weights samples by their influences to create a proper decision boundary. With these two improvements, we present the effective decision boundary learning algorithm (EDBL) which improves the performance of KD and deals with the imbalanced data learning simultaneously. Experiments show that the proposed EDBL achieves state-of-the-art performances on several CIL benchmarks.

翻译：类别增量学习（CIL）中的重放方法面临决策边界对新类别的过拟合问题，这主要源于两个因素：用于知识蒸馏的旧类数据不足，以及因存储内存限制导致已学类别与新类别之间的数据不平衡学习。本文提出一种简单而有效的方法来应对这两大因素。首先，我们采用重采样策略与混合知识蒸馏（Re-MKD）提升知识蒸馏性能，从而显著缓解过拟合问题。具体而言，我们将混合与重采样策略相结合，合成分布更符合已学类别与新类别之间潜在特征分布的充足数据用于知识蒸馏训练。其次，我们提出一种面向CIL的增量影响平衡（IIB）方法，通过将影响平衡方法扩展至CIL场景，根据样本影响度重新加权以构建恰当的决策边界，从而解决数据不平衡分类问题。通过这两项改进，我们提出有效决策边界学习算法（EDBL），该算法同时提升知识蒸馏性能并处理数据不平衡学习。实验表明，所提出的EDBL在多个CIL基准测试中达到了最先进性能。