Non-exemplar class-incremental learning refers to classifying new and old classes without storing samples of old classes. Since only new class samples are available for optimization, it often occurs catastrophic forgetting of old knowledge. To alleviate this problem, many new methods are proposed such as model distillation, class augmentation. In this paper, we propose an effective non-exemplar method called RAMF consisting of Random Auxiliary classes augmentation and Mixed Feature. On the one hand, we design a novel random auxiliary classes augmentation method, where one augmentation is randomly selected from three augmentations and applied on the input to generate augmented samples and extra class labels. By extending data and label space, it allows the model to learn more diverse representations, which can prevent the model from being biased towards learning task-specific features. When learning new tasks, it will reduce the change of feature space and improve model generalization. On the other hand, we employ mixed feature to replace the new features since only using new feature to optimize the model will affect the representation that was previously embedded in the feature space. Instead, by mixing new and old features, old knowledge can be retained without increasing the computational complexity. Extensive experiments on three benchmarks demonstrate the superiority of our approach, which outperforms the state-of-the-art non-exemplar methods and is comparable to high-performance replay-based methods.

本文提出了名为RAMF的非示例类增量学习方法，该方法包含随机辅助类增强和混合特征。通过对输入应用三种不同的增强方法，生成增强样本和额外的类标签，从而扩展数据和标签空间，减少特征空间变化并提高模型泛化能力。通过混合新旧特征，旧知识可以保留而不增加计算复杂度，在三个测试中，结果显示该方法优于其他方法并可与高性能重放方法相媲美。

随机辅助类别增强和混合特征的非典型类别增量学习