Class incremental learning consists in training discriminative models to classify an increasing number of classes over time. However, doing so using only the newly added class data leads to the known problem of catastrophic forgetting of the previous classes. Recently, dynamic deep learning architectures have been shown to exhibit a better stability-plasticity trade-off by dynamically adding new feature extractors to the model in order to learn new classes followed by a compression step to scale the model back to its original size, thus avoiding a growing number of parameters. In this context, we propose a new algorithm that enhances the compression of previous class knowledge by cutting and mixing patches of previous class samples with the new images during compression using our Rehearsal-CutMix method. We show that this new data augmentation reduces catastrophic forgetting by specifically targeting past class information and improving its compression. Extensive experiments performed on the CIFAR and ImageNet datasets under diverse incremental learning evaluation protocols demonstrate that our approach consistently outperforms the state-of-the-art . The code will be made available upon publication of our work.

我们提出了一种新算法，通过使用我们的Rehearsal-CutMix方法在压缩过程中切割和混合之前类别样本的图像补丁与新图像，来增强先前类别知识的压缩。在CIFAR和ImageNet数据集上进行的大量实验证明，我们的方法在不同的增量学习评估协议下始终优于现有技术。

特征扩展与增强的压缩对于类别增量学习的应用