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}{this https URL}

通过引入梯度相关子空间学习（GCSL）技术，本文介绍了一种减少增量类学习中灾难性遗忘的新方法，该方法检测受以前任务影响最小的权重的子空间，并将权重投影到该子空间进行新任务的训练，可适用于给定网络架构的一个或多个层，而且该子空间的大小可以在不同的层和任务之间进行调整。