在线持续学习与动态标签层次结构
Online Continual Learning with Dynamic Label Hierarchies
AI总结：
本文提出DHOCL框架，通过动态层次结构解决持续学习中的层次一致性与灾难性遗忘问题，HALO方法通过组织原型实现快速适应与知识整合。
摘要：
在线持续学习（OCL）旨在从无限非平稳数据流中学习，但现有方法假设扁平标签空间，忽略了现实世界概念的层次结构演变。本文引入DHOCL问题设置，其中分类学在不同粒度下演变，每个样本仅提供单一层次监督。发现两个关键问题：（i）混合粒度的局部监督仅提供路径演变中的点信号，限制可塑性并破坏跨层语义一致性；（ii）动态层次结构导致粒度依赖干扰，破坏流行重放和正则化机制，加剧灾难性遗忘。为解决这些问题，提出HALO（层次自适应学习与组织原型），通过自适应组合互补分类头，并通过组织可学习层次原型正则化，实现快速适应、层次一致性与结构化知识整合。在多个基准测试中，HALO在层次准确度、错误严重性和持续性能上均优于现有方法。
英文摘要：
Online Continual Learning (OCL) aims to learn from endless non\text{-}stationary data streams, yet most existing methods assume a flat label space and overlook the hierarchical organization of real\text{-}world concepts that evolves both horizontally (sibling classes) and vertically (coarse or fine categories). To better reflect this context, we introduce a new problem setting, DHOCL (Online Continual Learning from Dynamic Hierarchies), where taxonomies evolve across granularities and each sample provides supervision at a single hierarchical level. In this setting, we find two fundamental issues: (i) partial supervision under mixed granularities provides only point-wise signals over an evolving path-wise hierarchy, which constrains plasticity and undermines cross-level semantic consistency, and (ii) the dynamically evolving hierarchies induce granularity-dependent interference, destabilizing popular replay and regularization mechanisms and thereby exacerbating catastrophic forgetting. To tackle these issues, we propose HALO (Hierarchical Adaptive Learning with Organized Prototypes), which adaptively combines complementary classification heads, regularized by organized learnable hierarchical prototypes, enabling rapid adaptation, hierarchical consistency, and structured knowledge consolidation as the taxonomy evolves. Extensive experiments on multiple benchmarks demonstrate that HALO consistently outperforms existing methods across hierarchical accuracy, mistake severity, and continual performance.
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。
