Class-incremental learning (CIL) has achieved remarkable successes in learning new classes consecutively while overcoming catastrophic forgetting on old categories. However, most existing CIL methods unreasonably assume that all old categories have the same forgetting pace, and neglect negative influence of forgetting heterogeneity among different old classes on forgetting compensation. To surmount the above challenges, we develop a novel Heterogeneous Forgetting Compensation (HFC) model, which can resolve heterogeneous forgetting of easy-to-forget and hard-to-forget old categories from both representation and gradient aspects. Specifically, we design a task-semantic aggregation block to alleviate heterogeneous forgetting from representation aspect. It aggregates local category information within each task to learn task-shared global representations. Moreover, we develop two novel plug-and-play losses: a gradient-balanced forgetting compensation loss and a gradient-balanced relation distillation loss to alleviate forgetting from gradient aspect. They consider gradient-balanced compensation to rectify forgetting heterogeneity of old categories and heterogeneous relation consistency. Experiments on several representative datasets illustrate effectiveness of our HFC model. The code is available at https://github.com/JiahuaDong/HFC.

翻译：类增量学习（CIL）在连续学习新类别的同时克服旧类别灾难性遗忘方面取得了显著成功。然而，现有大多数CIL方法不合理地假设所有旧类别具有相同的遗忘速率，忽略了不同旧类别间遗忘异质性对遗忘补偿的负面影响。为攻克上述挑战，我们提出了一种新型异构遗忘补偿（HFC）模型，该模型能够从表征和梯度两个维度解决易遗忘与难遗忘旧类别的异构遗忘问题。具体而言，我们设计了任务语义聚合模块以缓解表征层面的异构遗忘，通过聚合每个任务内的局部类别信息来学习任务共享的全局表征。此外，我们提出了两种即插即用的新型损失函数：梯度平衡遗忘补偿损失与梯度平衡关系蒸馏损失，用于缓解梯度层面的遗忘。这两种损失函数通过梯度平衡补偿机制修正旧类别的遗忘异质性及异构关系一致性。在多个代表性数据集上的实验验证了HFC模型的有效性。代码已开源至https://github.com/JiahuaDong/HFC。