FeTrIL：面向无样本类增量学习的特征平移
FeTrIL: Feature Translation for Exemplar-Free Class-Incremental Learning
AI总结：
FeTrIL通过固定特征提取器并结合基于质心几何平移的伪特征生成器，在无样本类增量学习中平衡稳定性与可塑性，以更低成本取得优于主流方法的性能。
摘要：
无样本类增量学习由于灾难性遗忘的负面影响而极具挑战性。为了在旧类和新类上都获得良好的准确率，需要在增量过程的稳定性与可塑性之间取得平衡。现有的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，要么侧重于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。我们提出了一种结合固定特征提取器和伪特征生成器的方法，以改善稳定性与可塑性之间的平衡。该生成器利用对新类特征的简单而有效的几何平移来创建由伪特征构成的旧类表示。特征平移只需要存储旧类的质心表示即可生成其伪特征。新类的实际特征和旧类的伪特征被输入到一个线性分类器中，该分类器以增量方式训练以区分所有类别。与更新整个深度模型的主流方法相比，所提方法的增量过程要快得多。我们在三个具有挑战性的数据集和不同的增量设置下进行了实验。与十种现有方法的比较表明，我们的方法在大多数情况下优于其他方法。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
