The rehearsal strategy is widely used to alleviate the catastrophic forgetting problem in class incremental learning (CIL) by preserving limited exemplars from previous tasks. With imbalanced sample numbers between old and new classes, the classifier learning can be biased. Existing CIL methods exploit the long-tailed (LT) recognition techniques, e.g., the adjusted losses and the data re-sampling methods, to handle the data imbalance issue within each increment task. In this work, the dynamic nature of data imbalance in CIL is shown and a novel Dynamic Residual Classifier (DRC) is proposed to handle this challenging scenario. Specifically, DRC is built upon a recent advance residual classifier with the branch layer merging to handle the model-growing problem. Moreover, DRC is compatible with different CIL pipelines and substantially improves them. Combining DRC with the model adaptation and fusion (MAF) pipeline, this method achieves state-of-the-art results on both the conventional CIL and the LT-CIL benchmarks. Extensive experiments are also conducted for a detailed analysis. The code is publicly available.

翻译：重放策略通过保留先前任务的有限样本，被广泛用于缓解类增量学习（CIL）中的灾难性遗忘问题。由于新旧类之间的样本数量不均衡，分类器的学习可能会产生偏差。现有的CIL方法利用长尾（LT）识别技术（例如调整损失函数和数据重采样方法）来处理每个增量任务中的数据不平衡问题。本文揭示了CIL中数据不平衡的动态特性，并提出了一种新颖的动态残差分类器（DRC）来应对这一具有挑战性的场景。具体而言，DRC基于最新提出的残差分类器构建，并采用分支层合并策略来解决模型增长问题。此外，DRC兼容不同的CIL流水线，并能显著提升其性能。将DRC与模型适应与融合（MAF）流水线相结合，该方法在传统CIL和LT-CIL基准测试中均达到了最先进的水平。本文还进行了大量实验以进行详细分析。代码已公开。