Exemplar-free class-incremental learning is very challenging due to the negative effect of catastrophic forgetting. A balance between stability and plasticity of the incremental process is needed in order to obtain good accuracy for past as well as new classes. Existing exemplar-free class-incremental methods focus either on successive fine tuning of the model, thus favoring plasticity, or on using a feature extractor fixed after the initial incremental state, thus favoring stability. We introduce a method which combines a fixed feature extractor and a pseudo-features generator to improve the stability-plasticity balance. The generator uses a simple yet effective geometric translation of new class features to create representations of past classes, made of pseudo-features. The translation of features only requires the storage of the centroid representations of past classes to produce their pseudo-features. Actual features of new classes and pseudo-features of past classes are fed into a linear classifier which is trained incrementally to discriminate between all classes. The incremental process is much faster with the proposed method compared to mainstream ones which update the entire deep model. Experiments are performed with three challenging datasets, and different incremental settings. A comparison with ten existing methods shows that our method outperforms the others in most cases.

翻译：摘要：无样本类增量学习因灾难性遗忘的负面影响而极具挑战性。为了在旧类别与新类别上均取得良好精度，需要在增量过程的稳定性与可塑性之间取得平衡。现有无样本类增量方法要么侧重于对模型进行连续微调从而偏向可塑性，要么使用在初始增量状态后固定的特征提取器从而偏向稳定性。本文提出了一种结合固定特征提取器与伪特征生成器的方法，以改善稳定性-可塑性的平衡。该生成器采用简单而有效的几何翻译方法，将新类别的特征转换为由伪特征构成的旧类别表征。特征翻译仅需存储旧类别的质心表征即可生成其伪特征。新类别的真实特征与旧类别的伪特征被输入线性分类器，该分类器通过增量训练来区分所有类别。与更新整个深度模型的主流方法相比，本方法的增量过程速度显著提升。我们在三个具有挑战性的数据集及不同增量设置下开展了实验，与十种现有方法的对比表明，本方法在多数情况下表现更优。