eCIL-MU：基于嵌入的类增量学习与机器遗忘
eCIL-MU: Embedding based Class Incremental Learning and Machine Unlearning
AI总结：
针对动态环境中类增量学习（CIL）引入类级别机器遗忘（MU）时面临的耗时和灾难性遗忘问题，提出基于嵌入技术的非破坏性eCIL-MU框架，利用任务重叠实现约278倍加速并保持遗忘有效性。
摘要：
随着时间的推移，可能会引入新的类别，或者现有类别可能需要重新分类。类增量学习（CIL）用于在这种动态环境中逐步获取新类别的知识，同时保留关于先前学习类别的信息。为了适应重新分类，可能还需要消除相关类别对模型的影响。因此，我们在CIL中引入了类级别的机器遗忘（MU）。通常，MU方法往往很耗时，并且可能损害模型的性能。连续的遗忘请求流可能会导致灾难性遗忘。为了解决这些问题，我们提出了一个基于嵌入技术的非破坏性eCIL-MU框架，将数据映射为向量然后存储在向量数据库中。我们的方法利用CIL和MU任务之间的重叠来加速。实验证明了该方法能够实现遗忘有效性以及数量级（高达约278倍）的加速。
英文摘要：
New categories may be introduced over time, or existing categories may need to be reclassified. Class incremental learning (CIL) is employed for the gradual acquisition of knowledge about new categories while preserving information about previously learned ones in such dynamic environments. It might also be necessary to also eliminate the influence of related categories on the model to adapt to reclassification. We thus introduce class-level machine unlearning (MU) within CIL. Typically, MU methods tend to be time-consuming and can potentially harm the model's performance. A continuous stream of unlearning requests could lead to catastrophic forgetting. To address these issues, we propose a non-destructive eCIL-MU framework based on embedding techniques to map data into vectors and then be stored in vector databases. Our approach exploits the overlap between CIL and MU tasks for acceleration. Experiments demonstrate the capability of achieving unlearning effectiveness and orders of magnitude (upto $\sim 278\times$) of acceleration.
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。
