持续学习中的样本压缩
Sample Compression for Self Certified Continual Learning
AI总结：
本文提出基于样本压缩理论的CoP2L方法，通过系统保留代表性样本，为持续学习提供可计算的泛化损失上界，有效缓解灾难性遗忘并保证预测器可靠性。
摘要：
持续学习算法旨在从一系列任务中学习。为了避免灾难性遗忘，大多数现有方法依赖于启发式方法，并不提供可计算的学习保证。在本文中，我们引入了持续学习中的样本压缩理论（CoP2L），该方法以样本压缩理论为基础，以一种系统且高效的方式为每个任务保留代表性样本。这使我们能够推导出在每次任务后学习预测器的泛化损失的非空、数值可计算的上界。我们在标准的持续学习基准上评估了CoP2L，在类别增量和任务增量设置下，证明了它有效缓解了灾难性遗忘。结果表明，CoP2L在经验上与基线方法竞争，同时在持续学习中通过非空界认证预测器的可靠性。
英文摘要：
Continual learning algorithms aim to learn from a sequence of tasks. In order to avoid catastrophic forgetting, most existing approaches rely on heuristics and do not provide computable learning guarantees. In this paper, we introduce Continual Pick-to-Learn (CoP2L), a method grounded in sample compression theory that retains representative samples for each task in a principled and efficient way. This allows us to derive non-vacuous, numerically computable upper bounds on the generalization loss of the learned predictors after each task. We evaluate CoP2L on standard continual learning benchmarks under Class-Incremental and Task-Incremental settings, showing that it effectively mitigates catastrophic forgetting. It turns out that CoP2L is empirically competitive with baseline methods while certifying predictor reliability in continual learning with a non-vacuous bound.
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