Exemplar-Free Class Incremental Learning (efCIL) aims to continuously
incorporate the knowledge from new classes while retaining previously learned
information, without storing any old-class exemplars (i.e., samples). For this
purpose, various efCIL methods have been proposed over the past few years,
generally with elaborately constructed old pseudo-features, increasing the
difficulty of model development and interpretation. In contrast, we propose a
\textbf{simple Incremental Representation (IR) framework} for efCIL without
constructing old pseudo-features. IR utilizes dataset augmentation to cover a
suitable feature space and prevents the model from forgetting by using a single
L2 space maintenance loss. We discard the transient classifier trained on each
one of the sequence tasks and instead replace it with a 1-near-neighbor
classifier for inference, ensuring the representation is incrementally updated
during CIL. Extensive experiments demonstrate that our proposed IR achieves
comparable performance while significantly preventing the model from forgetting
on CIFAR100, TinyImageNet, and ImageNetSubset datasets.

通过使用简单的递增表示 (IR) 框架，使用数据增强覆盖合适的特征空间并使用单一的 L2 空间维护损失来防止模型遗忘，我们提出了一个用于非示例类增量学习 (efCIL) 的方法，实现在合并新类知识的同时保留先前学到的信息，而无需存储任何旧类示例 (样本)。在 CIFAR100、TinyImageNet 和 ImageNetSubset 数据集上广泛实验证明，我们提出的 IR 方法在性能上与其他方法相媲美，同时显著防止模型遗忘。