改进重放样本选择与存储以减少持续学习中的遗忘
Improving Replay Sample Selection and Storage for Less Forgetting in Continual Learning
AI总结：
本研究通过比较多种样本选择策略并提供寻找最佳存储样本数量的详细分析，改进了持续学习中的重放样本选择与存储，以减少灾难性遗忘。
摘要：
持续学习旨在使深度学习器能够在未知长度的一系列任务上进行训练，而不会遭受对先前任务的灾难性遗忘。一种有效的解决方案是重放，即将少量先前的经验存储在内存中，并在学习当前任务时重放它们。然而，在选择最具信息量的样本进行存储以及确定要存储的最佳样本数量方面，仍有改进空间。本研究旨在通过将常用的蓄水池采样与各种替代的种群策略进行新颖的比较，并提供关于如何找到最佳存储样本数量的新颖详细分析，来解决这些问题。
英文摘要：
Continual learning seeks to enable deep learners to train on a series of tasks of unknown length without suffering from the catastrophic forgetting of previous tasks. One effective solution is replay, which involves storing few previous experiences in memory and replaying them when learning the current task. However, there is still room for improvement when it comes to selecting the most informative samples for storage and determining the optimal number of samples to be stored. This study aims to address these issues with a novel comparison of the commonly used reservoir sampling to various alternative population strategies and providing a novel detailed analysis of how to find the optimal number of stored samples.
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。
