In class-incremental learning, a learning agent faces a stream of data with the goal of learning new classes while not forgetting previous ones. Neural networks are known to suffer under this setting, as they forget previously acquired knowledge. To address this problem, effective methods exploit past data stored in an episodic memory while expanding the final classifier nodes to accommodate the new classes. In this work, we substitute the expanding classifier with a novel fixed classifier in which a number of pre-allocated output nodes are subject to the classification loss right from the beginning of the learning phase. Contrarily to the standard expanding classifier, this allows: (a) the output nodes of future unseen classes to firstly see negative samples since the beginning of learning together with the positive samples that incrementally arrive; (b) to learn features that do not change their geometric configuration as novel classes are incorporated in the learning model. Experiments with public datasets show that the proposed approach is as effective as the expanding classifier while exhibiting novel intriguing properties of the internal feature representation that are otherwise not-existent. Our ablation study on pre-allocating a large number of classes further validates the approach.

翻译：在类增量学习中，学习智能体面临连续的数据流，其目标是在学习新类别的同时不遗忘先前学习过的类别。已知神经网络在此设定下会遭受遗忘先前知识的困扰。为解决此问题，有效方法会利用存储在情节记忆中的过往数据，同时扩展最终分类器节点以适应新类别。在本工作中，我们以新型固定分类器替代可扩展分类器，该分类器在初始学习阶段即对一定数量的预分配输出节点施加分类损失。与标准可扩展分类器相比，此方法能够：(a) 使未来未见类别的输出节点从学习伊始即能首先观察到负样本，同时逐步接收正样本；(b) 学习在新类别融入模型时保持几何配置不变的特征。基于公开数据集的实验表明，所提方法与可扩展分类器效果相当，同时展现出内部特征表示中新颖且独特的属性。针对大量类别预分配的消融研究进一步验证了该方法的有效性。