ER-FSL：面向在线持续学习的特征子空间学习经验回放
ER-FSL: Experience Replay with Feature Subspace Learning for Online Continual Learning
AI总结：
针对在线持续学习中的灾难性遗忘，提出ER-FSL方法，通过在不同特征空间学习和回放，利用子空间学习新知识并重用机制，在三个数据集上优于现有方法。
摘要：
在线持续学习（OCL）涉及深度神经网络在适应新数据的同时保留旧数据的知识，而新数据仅可访问一次。OCL中的一个关键挑战是灾难性遗忘，表现为模型在旧数据上的性能下降。现有的基于回放的方法通过回放旧数据的缓冲样本并学习新数据的当前样本来缓解遗忘。在本工作中，我们剖析了现有方法，并实证发现，在相同特征空间中学习和回放不利于解决遗忘问题。由于数据不平衡，与旧数据关联的学习特征容易被与新数据相关的特征改变，从而导致遗忘问题。基于这一观察，我们直观地探索在不同特征空间中学习和回放。在特征子空间中学习足以从新数据中捕获新颖知识，而在更大特征空间中回放则提供更多特征空间来维护来自旧数据的历史知识。为此，我们提出了一种新颖的OCL方法，称为特征子空间学习经验回放（ER-FSL）。首先，ER-FSL将整个特征空间划分为多个子空间，每个子空间用于学习当前样本。此外，它引入了一种子空间重用机制，以处理不存在空白子空间的情况。其次，ER-FSL使用包含所有已学习子空间的累积空间来回放先前样本。在三个数据集上的大量实验证明了ER-FSL相对于各种最先进方法的优越性。
英文摘要：
Online continual learning (OCL) involves deep neural networks retaining knowledge from old data while adapting to new data, which is accessible only once. A critical challenge in OCL is catastrophic forgetting, reflected in reduced model performance on old data. Existing replay-based methods mitigate forgetting by replaying buffered samples from old data and learning current samples of new data. In this work, we dissect existing methods and empirically discover that learning and replaying in the same feature space is not conducive to addressing the forgetting issue. Since the learned features associated with old data are readily changed by the features related to new data due to data imbalance, leading to the forgetting problem. Based on this observation, we intuitively explore learning and replaying in different feature spaces. Learning in a feature subspace is sufficient to capture novel knowledge from new data while replaying in a larger feature space provides more feature space to maintain historical knowledge from old data. To this end, we propose a novel OCL approach called experience replay with feature subspace learning (ER-FSL). Firstly, ER-FSL divides the entire feature space into multiple subspaces, with each subspace used to learn current samples. Moreover, it introduces a subspace reuse mechanism to address situations where no blank subspaces exist. Secondly, ER-FSL replays previous samples using an accumulated space comprising all learned subspaces. Extensive experiments on three datasets demonstrate the superiority of ER-FSL over various state-of-the-art methods.
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。
