In neural networks, continual learning results in gradient interference among
sequential tasks, leading to catastrophic forgetting of old tasks while
learning new ones. This issue is addressed in recent methods by storing the
important gradient spaces for old tasks and updating the model orthogonally
during new tasks. However, such restrictive orthogonal gradient updates hamper
the learning capability of the new tasks resulting in sub-optimal performance.
To improve new learning while minimizing forgetting, in this paper we propose a
Scaled Gradient Projection (SGP) method, where we combine the orthogonal
gradient projections with scaled gradient steps along the important gradient
spaces for the past tasks. The degree of gradient scaling along these spaces
depends on the importance of the bases spanning them. We propose an efficient
method for computing and accumulating importance of these bases using the
singular value decomposition of the input representations for each task. We
conduct extensive experiments ranging from continual image classification to
reinforcement learning tasks and report better performance with less training
overhead than the state-of-the-art approaches.

本研究提出了一种基于 Scaled Gradient Projection 方法的神经网络演化学习算法，通过横跨重要梯度空间的基向量缩放梯度来避免老任务信息的灾难性遗忘，优化新学习表现，实验证明其在各种任务中性能均优于目前最先进的方法。