AdaER：一种面向持续终身学习的自适应经验回放方法
AdaER: An Adaptive Experience Replay Approach for Continual Lifelong Learning
AI总结：
针对持续终身学习中的灾难性遗忘问题，本文提出AdaER算法，通过C-CMR选择性回放冲突记忆并以E-BRS最大化缓冲信息熵，在类别增量基准上优于现有基线。
摘要：
持续终身学习是一种受人类学习启发的机器学习框架，在该框架中，学习器经过训练，以按顺序的方式持续获取新知识。然而，流式训练数据的非平稳特性带来了一项被称为灾难性遗忘的重大挑战，即当引入新任务时，先前学到的知识会被迅速遗忘。尽管经验回放（ER）等一些方法已被提出以缓解这一问题，但其性能仍然有限，尤其是在被认为自然且极具挑战性的类别增量场景中。本文提出一种名为自适应经验回放（AdaER）的新型算法，以应对持续终身学习的挑战。AdaER包含两个阶段：记忆回放和记忆更新。在记忆回放阶段，AdaER引入了上下文线索记忆回忆（C-CMR）策略，该策略从数据和任务两个方面选择性地回放与当前输入数据冲突最大的记忆。此外，AdaER结合了熵平衡水库采样（E-BRS）策略，通过最大化信息熵来提升记忆缓冲区的性能。为评估AdaER的有效性，我们在成熟的有监督持续终身学习基准上开展实验，特别聚焦于类别增量学习场景。结果表明，AdaER优于现有的持续终身学习基线，凸显了其在缓解灾难性遗忘和提升学习性能方面的功效。
英文摘要：
Continual lifelong learning is an machine learning framework inspired by human learning, where learners are trained to continuously acquire new knowledge in a sequential manner. However, the non-stationary nature of streaming training data poses a significant challenge known as catastrophic forgetting, which refers to the rapid forgetting of previously learned knowledge when new tasks are introduced. While some approaches, such as experience replay (ER), have been proposed to mitigate this issue, their performance remains limited, particularly in the class-incremental scenario which is considered natural and highly challenging. In this paper, we present a novel algorithm, called adaptive-experience replay (AdaER), to address the challenge of continual lifelong learning. AdaER consists of two stages: memory replay and memory update. In the memory replay stage, AdaER introduces a contextually-cued memory recall (C-CMR) strategy, which selectively replays memories that are most conflicting with the current input data in terms of both data and task. Additionally, AdaER incorporates an entropy-balanced reservoir sampling (E-BRS) strategy to enhance the performance of the memory buffer by maximizing information entropy. To evaluate the effectiveness of AdaER, we conduct experiments on established supervised continual lifelong learning benchmarks, specifically focusing on class-incremental learning scenarios. The results demonstrate that AdaER outperforms existing continual lifelong learning baselines, highlighting its efficacy in mitigating catastrophic forgetting and improving learning performance.
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。
