Less Over More: Interference Sample Gradient Purification For Parallel Continual Learning

Abstract

The goal of Parallel Continual Learning (PCL) is to continually learn multi-task from new data stream and complete the corresponding tasks. Previous research on PCL ignored inter-task interference, which may hinder knowledge transfer and exacerbate catastrophic forgetting. Therefore, in this paper, we investigate the interference problem of PCL in dynamic multi-task scenarios. First, we construct Global Discrimination Threshold to detect interference sample, and removing the interference gradient in joint multi-gradient algorithm, termed Interference Sample Gradient Purification (ISGP). Second, we introduced the Guardian For Pivotal Memory Sample (G-PMS) as new evaluation criterion and proposed Local Maintenance Thresholds to prevent accidental exclusion of memory samples that play an important role in suppressing catastrophic forgetting. Finally, the results of the experimental evaluation clearly confirm that ISGP can effectively enhance the transfer of new knowledge and better suppress catastrophic forgetting.