In continual learning, many classifiers use softmax function to learn confidence. However, numerous studies have pointed out its inability to accurately determine confidence distributions for outliers, often referred to as epistemic uncertainty. This inherent limitation also curtails the accurate decisions for selecting what to forget and keep in previously trained confidence distributions over continual learning process. To address the issue, we revisit the effects of masking softmax function. While this method is both simple and prevalent in literature, its implication for retaining confidence distribution during continual learning, also known as stability, has been under-investigated. In this paper, we revisit the impact of softmax masking, and introduce a methodology to utilize its confidence preservation effects. In class- and task-incremental learning benchmarks with and without memory replay, our approach significantly increases stability while maintaining sufficiently large plasticity. In the end, our methodology shows better overall performance than state-of-the-art methods, particularly in the use with zero or small memory. This lays a simple and effective foundation of strongly stable replay-based continual learning.

翻译：在持续学习中，多数分类器采用softmax函数进行置信度学习。然而，大量研究指出该函数难以准确判定异常值的置信分布，这种现象通常被称为认知不确定性。这一固有缺陷同样限制了在持续学习过程中，模型对先前学习的置信分布做出"遗忘"与"保留"的精准决策。为解决该问题，我们重新审视了softmax掩码技术的实际效果。尽管该方法在文献中既简单又普遍，但其对持续学习过程中置信分布保持（即稳定性）的深层影响尚未得到充分研究。本文深入探讨了softmax掩码的作用机理，并提出了一种利用其置信度保持特性的方法论。在含/不含记忆回放的类增量学习与任务增量学习基准测试中，我们的方法在维持足够可塑性的同时显著提升了稳定性。最终实验证明，该方法在零记忆或小记忆场景下的综合性能优于当前最先进的技术，为基于记忆回放的强稳定性持续学习奠定了简洁高效的基础。