超越类别边际：在不冻结类别共现的情况下界定重演间隔

中文摘要 AI 辅助

类别平衡重放控制类别频率，但不决定一个类别连续重放出现之间的间隔。我们将这个间隔（即重演间隔）与类别边际和类别共现分开研究，并引入随机遍历重放（RPR），该方法在每次打乱顺序的遍历中访问每个驻留类别一次。对于固定的C个驻留类别集合和小于或等于C的重放批次大小b，RPR保持平衡的时间平均类别边际，并将每个间隔界定在2*ceil(C/b)-1以内；在驻留集合变化时，适用一个基于变动的边界。该调度器不使用未来类别信息，也不增加重放示例或前向传播。在线性头ER-ACE诊断中，同时缺席于输入批次和重放批次会产生单侧分类器偏差梯度。较长的缺席时段与较大的负偏差位移相关，而移除输入损失掩码会减弱调度效应。在主要的ER-ACE实验中，与独立类别平衡检索（使用储层存储）相比，RPR将最终平均准确率提高了0.72-1.67个百分点，在平衡存储下也观察到积极效果。预训练ViT在测试的LT10流上（使用小重放批次）显示出积极效果，而匹配的大批次对照组没有实质性效果。固定周期和重用遍历对照组改变了多个时间统计量，因此实验未能将重演间隔长度与所有其他形式的时间依赖性分离开来。准确率效果取决于学习器和运行机制。

英文摘要

Class-balanced replay controls class frequency but does not determine the interval between successive replay appearances of a class. We study this interval, the rehearsal gap, separately from the class marginal and class co-occurrence, and introduce randomised-pass replay (RPR), which visits each resident class once per shuffled pass. For a fixed set of C resident classes and replay batch size b less than or equal to C, RPR preserves the balanced time-averaged class marginal and bounds every gap by 2*ceil(C/b)-1; a churn-conditional bound applies while the resident set changes. The scheduler uses no future class information and adds no replay examples or forward passes. In a linear-head ER-ACE diagnostic, joint absence from the incoming and replay batches produces a one-sided classifier-bias gradient. Longer absence episodes are associated with larger negative bias displacement, and removing the incoming-loss mask attenuates the scheduling effect. In the primary ER-ACE experiments, RPR improves final average accuracy by 0.72-1.67 percentage points relative to independent class-balanced retrieval under reservoir storage, with positive effects also observed under balanced storage. Pretrained ViTs show positive effects on the tested LT10 streams with small replay batches, while matched larger-batch controls show no material effect. Fixed-cycle and reused-pass controls change more than one temporal statistic, so the experiments do not isolate rehearsal-gap length from all other forms of temporal dependence. The accuracy effects depend on the learner and operating regime.

发表机构

• Imperial College London（帝国理工学院）
• King’s College London（伦敦国王学院）
• University of Electronic Science and Technology of China（电子科技大学）

机构由 AI 辅助整理，请以论文原文为准。