用自然梯度优化缓解在线学习中的灾难性遗忘问题
Natural Gradient Descent for Online Continual Learning
- 采用自然梯度结合KFAC近似费舍尔信息矩阵
- 在Split CIFAR-100等数据集上显著提升性能
- 适合追求高效持续学习的算法研究者
在线持续学习（OCL）针对图像分类任务，面临非独立同分布数据流下的持续学习挑战。核心问题是防止灾难性遗忘——模型在学习新任务时遗忘旧任务。尽管已有多种方法缓解该问题，但在线场景中实现快速收敛仍具挑战。本文提出一种新训练方法：使用自然梯度下降优化器，并通过克罗内克积近似曲率（KFAC）估算费舍尔信息矩阵（FIM）。该方法在多个主流数据集（如Split CIFAR-100、CORE50、Split miniImageNet）上均显著提升现有OCL方法的性能，尤其与已有持续学习技巧结合时效果更优。
原文摘要 · Abstract (English)
Online Continual Learning (OCL) for image classification represents a challenging subset of Continual Learning, focusing on classifying images from a stream without assuming data independence and identical distribution (i.i.d). The primary challenge in this context is to prevent catastrophic forgetting, where the model's performance on previous tasks deteriorates as it learns new ones. Although various strategies have been proposed to address this issue, achieving rapid convergence remains a significant challenge in the online setting. In this work, we introduce a novel approach to training OCL models that utilizes the Natural Gradient Descent optimizer, incorporating an approximation of the Fisher Information Matrix (FIM) through Kronecker Factored Approximate Curvature (KFAC). This method demonstrates substantial improvements in performance across all OCL methods, particularly when combined with existing OCL tricks, on datasets such as Split CIFAR-100, CORE50, and Split miniImageNet.
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