对抗灾难性遗忘的梯度相关子空间学习
Gradient Correlation Subspace Learning against Catastrophic Forgetting
AI总结：
本文提出GCSL方法，通过寻找受旧任务影响最小的权重子空间并投影新任务训练权重，以缓解增量类别学习中的灾难性遗忘。
摘要：
在过去几年中，高效的持续学习技术一直是重要的研究课题。此类学习的一个根本问题是先前已学任务上的性能严重下降，这也被称为灾难性遗忘。本文在增量类别学习的背景下提出了一种减轻灾难性遗忘的新方法，称为梯度相关子空间学习（Gradient Correlation Subspace Learning，GCSL）。该方法检测权重中受先前任务影响最小的子空间，并将用于训练新任务的权重投影到该子空间中。该方法可应用于给定网络架构的一个或多个层，并且所使用子空间的大小可以逐层、逐任务调整。代码将在 https://github.com/vgthengane/GCSL 提供。
英文摘要：
Efficient continual learning techniques have been a topic of significant research over the last few years. A fundamental problem with such learning is severe degradation of performance on previously learned tasks, known also as catastrophic forgetting. This paper introduces a novel method to reduce catastrophic forgetting in the context of incremental class learning called Gradient Correlation Subspace Learning (GCSL). The method detects a subspace of the weights that is least affected by previous tasks and projects the weights to train for the new task into said subspace. The method can be applied to one or more layers of a given network architectures and the size of the subspace used can be altered from layer to layer and task to task. Code will be available at \href{https://github.com/vgthengane/GCSL}{https://github.com/vgthengane/GCSL}
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。
