Smart Replay：面向计算资源感知增量学习的自适应记忆复述调度
Smart Replay: Adaptive Scheduling of Memory Rehearsal for Computational Resource-Aware Incremental Learning
把回放比例当作可调度控制变量、用启发式Q函数在训练中周期性调整，为视觉增量学习在固定算力预算下提供了一种即插即用的调度策略，可直接嵌入基于回放的类增量分类/检测流程。
原文依据 we propose Smart Replay, an efficient memory rehearsal algorithm that adaptively allocates resources by scheduling the replay ratio across mini-batches
摘要译文
由模型翻译 · 术语统一 · 请对照原文核对关键结论增量学习（IL）源于在有限数据和计算资源下持续更新模型的需求。现有大多数增量学习研究关注数据稀缺场景，往往设计依赖大量计算的复杂方法，却忽视了真实场景中常见的计算资源约束。这促使我们将计算资源感知增量学习这一问题形式化，在模型训练过程中显式考虑计算预算。为解决该问题，我们提出 Smart Replay，一种高效的内存复述算法，通过在小批次间调度回放比例来自适应分配资源。我们将回放比例优化建模为最优控制问题，联合最小化新任务损失与记忆损失。我们进一步提出一种启发式 Q 函数来指导比例调整，自适应地平衡短期效率与长期稳定性。最后，我们开发了一种在训练过程中周期性更新回放比例的实用算法。在多个基准上的实验验证，Smart Replay 在相同计算预算下持续优于固定回放基线，取得更高准确率与更低遗忘。
英文原文摘要 1241 字符
Incremental learning (IL) arises from the need to continuously update models under limited data and computational resources. Most existing IL studies focus on data-scarce settings. They often develop complex methods that rely on heavy computation, while overlooking the computational resource constraints common in real-world scenarios. This motivates us to formalize the problem of Computational Resource-Aware Incremental Learning, which explicitly considers the computational budget during model training. To tackle this problem, we propose Smart Replay, an efficient memory rehearsal algorithm that adaptively allocates resources by scheduling the replay ratio across mini-batches. We cast replay-ratio optimization into an optimal control formulation that jointly minimizes new-task and memory losses. We further propose a heuristic Q-function to guide ratio adjustments, adaptively balancing short-term efficiency and long-term stability. Finally, we develop a practical algorithm that periodically updates the replay ratio during training. Experiments on multiple benchmarks validate that Smart Replay consistently outperforms fixed-replay baselines, achieving higher accuracy and lower forgetting under the same computational budget.