新方法让模型学新类时既不遗忘旧知识，又保持快速推理。
Adapter Merging with Centroid Prototype Mapping for Scalable Class-Incremental Learning
- 用适配器融合构建共享表征空间，统一多阶段学习。
- 在五个数据集上达到顶尖准确率，推理时间恒定不变。
- 适合需要持续学习且对速度敏感的工业场景应用。
我们提出一种无实例的类别增量学习框架 ACMap，解决灾难性遗忘与可扩展性问题。现有方法在推理速度和准确率间存在权衡，而 ACMap 将各任务专用适配器合并为单一适配器，实现跨任务恒定推理时间且不损失精度。该框架通过适配器融合建立共享子空间以对齐任务表征、缓解遗忘，同时利用中心原型映射在共享空间中持续调整表征以保持高精度。为进一步提升可扩展性，引入早停策略控制适配器融合过程随任务增加的规模。在五个基准数据集上的大量实验表明，ACMap 达到当前最优准确率，同时推理时间与最快现有方法相当。代码已公开于 https://github.com/tf63/ACMap。
原文摘要 · Abstract (English)
We propose Adapter Merging with Centroid Prototype Mapping (ACMap), an exemplar-free framework for class-incremental learning (CIL) that addresses both catastrophic forgetting and scalability. While existing methods involve a trade-off between inference time and accuracy, ACMap consolidates task-specific adapters into a single adapter, thus achieving constant inference time across tasks without sacrificing accuracy. The framework employs adapter merging to build a shared subspace that aligns task representations and mitigates forgetting, while centroid prototype mapping maintains high accuracy by consistently adapting representations within the shared subspace. To further improve scalability, an early stopping strategy limits adapter merging as tasks increase. Extensive experiments on five benchmark datasets demonstrate that ACMap matches state-of-the-art accuracy while maintaining inference time comparable to the fastest existing methods. The code is available at https://github.com/tf63/ACMap.
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