English(EN) REDAR: Reinforcement-Driven Adaptive Regularisation for Class Incremental Learning

新的REDAR方法使用强化学习进行类别增量学习中的自适应正则化

作者 PulseAugur 编辑部 · [1 个来源] · 2026-10-27 04:00

研究人员推出了一种新颖的类别增量学习方法REDAR，该方法利用强化学习动态调整正则化系数。与依赖固定系数的传统方法不同，REDAR采用RL代理来观察性能指标和数据统计信息，为每个增量阶段输出最优系数。这种自适应策略旨在更好地管理稳定-可塑性权衡，减少遗忘并增强前向迁移，如在CIFAR-100和Tiny-ImageNet数据集上的实验所示。 AI

影响为类别增量学习引入了一种新颖的自适应正则化技术，有可能在数据分布不断变化的场景中提高模型性能。

排序理由该集群包含一篇详细介绍类别增量学习新方法的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.LG 阅读 →

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报道来源 [1]

arXiv cs.LG TIER_1 English(EN) · Akila Ayanthi, Darshana Priyasad, Tharindu Fernando, Sridha Sridharan, Clinton Fookes, Peyman Moghadam · 2026-10-27 04:00

REDAR：用于类别增量学习的强化驱动自适应正则化

arXiv:2610.31991v1 Announce Type: new Abstract: Regularisation-based class-incremental learning (CIL) methods control the stability-plasticity trade-off through fixed coefficients tuned offline, independently of the continual learning objective and blind to phase-specific variati…

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REDAR：用于类别增量学习的强化驱动自适应正则化

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