Class-incremental learning (CIL) is a particularly challenging variant of continual learning, where the goal is to learn to discriminate between all classes presented in an incremental fashion. Existing approaches often suffer from excessive forgetting and imbalance of the scores assigned to classes that have not been seen together during training. In this study, we introduce a novel approach, Prediction Error-based Classification (PEC), which differs from traditional discriminative and generative classification paradigms. PEC computes a class score by measuring the prediction error of a model trained to replicate the outputs of a frozen random neural network on data from that class. The method can be interpreted as approximating a classification rule based on Gaussian Process posterior variance. PEC offers several practical advantages, including sample efficiency, ease of tuning, and effectiveness even when data are presented one class at a time. Our empirical results show that PEC performs strongly in single-pass-through-data CIL, outperforming other rehearsal-free baselines in all cases and rehearsal-based methods with moderate replay buffer size in most cases across multiple benchmarks.

翻译：类增量学习（CIL）是持续学习中一项极具挑战性的变体，其目标是以增量方式学会区分所有已呈现的类别。现有方法常面临严重的遗忘问题，以及训练过程中未曾同时出现的类别之间分数分配不平衡的问题。在本研究中，我们提出了一种新颖方法——基于预测误差的分类（PEC），该方法有别于传统的判别式和生成式分类范式。PEC通过测量一个模型在特定类别数据上复制冻结随机神经网络输出时的预测误差，来计算该类别的得分。该方法的原理可解释为基于高斯过程后验方差对分类规则进行近似。PEC具有若干实际优势，包括样本效率高、易于调参，甚至在数据逐类别呈现的情况下也能保持有效性。我们的实验结果表明，PEC在单次遍历数据的类增量学习中表现优异，在所有无重放基线的对比任务中均胜出，且在多个基准测试的大多数场景下，其表现优于采用中等重放缓存大小的重放基类方法。