Class-incremental learning is dedicated to the development of deep learning models that are capable of acquiring new knowledge while retaining previously learned information. Most methods focus on balanced data distribution for each task, overlooking real-world long-tailed distributions. Therefore, Long-Tailed Class-Incremental Learning has been introduced, which trains on data where head classes have more samples than tail classes. Existing methods mainly focus on preserving representative samples from previous classes to combat catastrophic forgetting. Recently, dynamic network algorithms freeze old network structures and expand new ones, achieving significant performance. However, with the introduction of the long-tail problem, merely extending Determined blocks can lead to miscalibrated predictions, while expanding the entire backbone results in an explosion of memory size. To address these issues, we introduce a novel Task-aware Expandable (TaE) framework, dynamically allocating and updating task-specific trainable parameters to learn diverse representations from each incremental task while resisting forgetting through the majority of frozen model parameters. To further encourage the class-specific feature representation, we develop a Centroid-Enhanced (CEd) method to guide the update of these task-aware parameters. This approach is designed to adaptively allocate feature space for every class by adjusting the distance between intra- and inter-class features, which can extend to all "training from sketch" algorithms. Extensive experiments demonstrate that TaE achieves state-of-the-art performance.

翻译：类增量学习致力于开发能够获取新知识同时保留先前学习信息的深度学习模型。大多数方法关注每个任务的平衡数据分布，忽视了现实世界中的长尾分布。因此，长尾类增量学习被提出，其在头部类比尾部类拥有更多样本的数据上进行训练。现有方法主要侧重于保留先前类别的代表性样本来对抗灾难性遗忘。最近，动态网络算法通过冻结旧网络结构并扩展新结构，取得了显著性能。然而，随着长尾问题的引入，仅扩展Determined模块会导致预测校准偏差，而扩展整个骨干网络则会引起内存规模爆炸。为解决这些问题，我们提出了一种新颖的任务感知可扩展（TaE）框架，动态分配和更新任务特定的可训练参数，以从每个增量任务中学习多样化表示，同时通过大部分冻结模型参数抵抗遗忘。为进一步增强类别特定的特征表示，我们开发了质心增强（CEd）方法，以指导这些任务感知参数的更新。该方法通过调整类内与类间特征距离，自适应地为每个类别分配特征空间，可扩展至所有"从零开始训练"的算法。大量实验表明，TaE实现了最先进的性能。