基于对比提示的无排练无任务在线持续学习
Rehearsal-free and Task-free Online Continual Learning With Contrastive Prompt
AI总结：
针对在线持续学习的灾难性遗忘难题，提出无排练无任务的F2OCL方法，结合提示学习与NCM分类器，在无需存储样本和任务信息的情况下实现有效学习，经双基准实验验证效果。
摘要：
持续学习的核心挑战是灾难性遗忘。由于仅对数据进行单次遍历处理，在线持续学习（OCL）是难度最高的持续学习场景之一。为解决OCL中的灾难性遗忘问题，现有研究部分采用排练缓冲区存储样本并在后续学习过程中重放，另一部分不存储样本但假设存在学习任务序列，以利用任务身份信息。但存储样本可能引发数据安全或隐私问题，且在单次数据处理流程中并非总能识别学习任务的边界。这推动我们研究无排练无任务在线持续学习（F2OCL）。本研究将提示学习与NCM（最近类均值）分类器相结合，在无需存储样本、无需使用任务边界或身份信息的前提下，有效解决了灾难性遗忘问题。在两个基准上的大量实验结果验证了所提方法的有效性。
英文摘要：
The main challenge of continual learning is \textit{catastrophic forgetting}. Because of processing data in one pass, online continual learning (OCL) is one of the most difficult continual learning scenarios. To address catastrophic forgetting in OCL, some existing studies use a rehearsal buffer to store samples and replay them in the later learning process, other studies do not store samples but assume a sequence of learning tasks so that the task identities can be explored. However, storing samples may raise data security or privacy concerns and it is not always possible to identify the boundaries between learning tasks in one pass of data processing. It motivates us to investigate rehearsal-free and task-free OCL (F2OCL). By integrating prompt learning with an NCM classifier, this study has effectively tackled catastrophic forgetting without storing samples and without usage of task boundaries or identities. The extensive experimental results on two benchmarks have demonstrated the effectiveness of the proposed method.
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。
