← 研踪 PaperTrailCVF · CVPR 2026:cvf:3c3f08a3506327b18a5fa096ae7cb4ea · 记录 #1
Smart Replay: Adaptive Scheduling of Memory Rehearsal for Computational Resource-Aware Incremental Learning
来源日期（仅到年份） 2026
来源记录日期 2026
雷达观测 2026-11-05 04:03
摘要
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.
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记录 #1 · 2026 · 最新观测版本
Smart Replay: Adaptive Scheduling of Memory Rehearsal for Computational Resource-Aware Incremental Learning
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.
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