Class incremental learning (CIL) aims to recognize both the old and new classes along the increment tasks. Deep neural networks in CIL suffer from catastrophic forgetting and some approaches rely on saving exemplars from previous tasks, known as the exemplar-based setting, to alleviate this problem. On the contrary, this paper focuses on the Exemplar-Free setting with no old class sample preserved. Balancing the plasticity and stability in deep feature learning with only supervision from new classes is more challenging. Most existing Exemplar-Free CIL methods report the overall performance only and lack further analysis. In this work, different methods are examined with complementary metrics in greater detail. Moreover, we propose a simple CIL method, Rotation Augmented Distillation (RAD), which achieves one of the top-tier performances under the Exemplar-Free setting. Detailed analysis shows our RAD benefits from the superior balance between plasticity and stability. Finally, more challenging exemplar-free settings with fewer initial classes are undertaken for further demonstrations and comparisons among the state-of-the-art methods.

翻译：类增量学习（CIL）旨在在增量任务中同时识别新旧类别。深度神经网络在类增量学习中面临灾难性遗忘问题，一些方法依赖于保存先前任务的样本（即基于示例的设置）来缓解该问题。相反，本文聚焦于无示例设置，即不保留任何旧类别样本。在仅依赖新类别监督的深度特征学习中平衡可塑性与稳定性更具挑战性。现有大多数无示例类增量学习方法仅报告总体性能而缺乏深入分析。本研究采用互补指标对多种方法进行了更细致的评估。此外，我们提出了一种简单的类增量学习方法——旋转增强蒸馏（RAD），该方法在无示例设置下达到了顶尖性能水平。详细分析表明，我们的RAD方法得益于可塑性与稳定性之间的卓越平衡。最后，我们进一步在有更少初始类别、更具挑战性的无示例设置下进行了实验，以展示并比较当前最优方法的表现。