The Effect of Task Ordering in Continual Learning
This work addresses a key challenge in continual learning for AI systems, offering an incremental improvement through task ordering optimization.
The study tackled the problem of catastrophic forgetting in continual learning by investigating how task ordering affects performance, finding that reordering tasks significantly reduces forgetting and presenting a method to exploit this for improved results.
We investigate the effect of task ordering on continual learning performance. We conduct an extensive series of empirical experiments on synthetic and naturalistic datasets and show that reordering tasks significantly affects the amount of catastrophic forgetting. Connecting to the field of curriculum learning, we show that the effect of task ordering can be exploited to modify continual learning performance, and present a simple approach for doing so. Our method computes the distance between all pairs of tasks, where distance is defined as the source task curvature of a gradient step toward the target task. Using statistically rigorous methods and sound experimental design, we show that task ordering is an important aspect of continual learning that can be modified for improved performance.