One of the most widely used approaches in continual learning is referred to as replay. Replay methods support interleaved learning by storing past experiences in a replay buffer. Although there are methods for selectively constructing the buffer and reprocessing its contents, there is limited exploration of the problem of selectively retrieving samples from the buffer. Current solutions have been tested in limited settings and, more importantly, in isolation. Existing work has also not explored the impact of duplicate replays on performance. In this work, we propose a framework for evaluating selective retrieval strategies, categorized by simple, independent class- and sample-selective primitives. We evaluated several combinations of existing strategies for selective retrieval and present their performances. Furthermore, we propose a set of strategies to prevent duplicate replays and explore whether new samples with low loss values can be learned without replay. In an effort to match our problem setting to a realistic continual learning pipeline, we restrict our experiments to a setting involving a large, pre-trained, open vocabulary object detection model, which is fully fine-tuned on a sequence of 15 datasets.

翻译：持续学习中最广泛使用的方法之一被称为重放。重放方法通过将过去的经验存储在重放缓冲区中，支持交错学习。尽管已有选择性构建缓冲区及重新处理其内容的方法，但对于从缓冲区中选择性检索样本这一问题的探索仍然有限。现有解决方案仅在有限场景下经过测试，且更为关键的是，它们均处于孤立测试状态。现有工作也未探讨重复重放对性能的影响。在本研究中，我们提出一个评估选择性检索策略的框架，该框架依据简单的独立类级与样本级选择原语进行分类。我们评估了现有选择性检索策略的多种组合，并展示了其性能。此外，我们提出了一套防止重复重放的策略，并探索了低损失值的新样本是否能在无需重放的情况下被学习。为使其问题设置更贴近真实持续学习流程，我们将实验限定在一种大规模预训练开放词汇目标检测模型的场景中，该模型在包含15个数据集的序列上进行了完整微调。