Watch Your Step：面向大规模持续学习的最优检索
Watch Your Step: Optimal Retrieval for Continual Learning at Scale
AI总结：
本文提出大规模持续学习中的选择性回放检索评估框架，系统比较类别与样本级策略，研究重复回放及低损失新样本免回放学习，并在15个数据集序列上全面微调开放词汇表检测模型。
摘要：
在持续学习中，模型随时间增量学习，同时尽量减少旧任务与新任务之间的干扰。持续学习中最广泛使用的方法之一称为回放。回放方法通过将过去的经验存储在回放缓冲区中来支持交错学习。尽管已有一些方法用于选择性地构建缓冲区并重新处理其内容，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。当前解决方案只在有限的设置中接受测试，更重要的是，它们都是孤立测试的。现有工作也没有探索重复回放对性能的影响。在本文中，我们提出了一个用于评估选择性检索策略的框架，这些策略按简单、独立的类别选择性原语和样本选择性原语进行分类。我们评估了现有选择性检索策略的若干组合，并给出了它们的性能。此外，我们提出了一组防止重复回放的策略，并探索低损失值的新样本是否可以在不回放的情况下完成学习。为了使我们的问题设置与现实的持续学习流程相匹配，我们将实验限制在一个涉及大型、预训练、开放词汇表目标检测模型的设置中，该模型在由15个数据集组成的序列上进行全面微调。
英文摘要：
In continual learning, a model learns incrementally over time while minimizing interference between old and new tasks. 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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
