A Hybrid Framework for IA Drives Improved Elastic Weight Consolidation and Dynamic Knowledge Distillation Improvements

Abstract

Aiming at the problems of high computational complexity of traditional elastic weight consolidation (EWC), weak generalization ability of AdaDistill, and poor adaptability of non-IID data in continuous learning, an improved hybrid framework driven by Intelligence Augmentation (IA) is proposed. The framework optimizes the parameter protection strategy of EWC through the meta-learning adaptive mechanism, introduces reinforcement learning dynamic regulation to improve the knowledge transfer efficiency of AdaDistill, and designs an adaptive weight fusion module to achieve collaborative optimization between the two. Specifically, in the EWC module, an online sparse Fisher information estimation method is proposed, which reduces the computational complexity from O(K×N²) to O(L×N).  In the AdaDistill module, a multi-teacher collaborative distillation and dynamic temperature control mechanism is built to improve the ability of cross-task generalization. The total loss weight was adjusted by the dual feedback of task similarity and learning progress, and the adaptability of non-IID data was enhanced. Experiments on the Split CIFAR-100, Permuted MNIST, and GLUE datasets show that the average accuracy of the framework is improved by 7.2%~9.5%, the forgetting rate is reduced by 42.3%~51.6%, the training time is shortened by 65.8%~73.4%, and the memory usage is reduced by 78.2%~85.1% compared with the traditional EWC and AdaDistill. This framework can provide efficient solutions for continuous learning scenarios such as autonomous driving and medical diagnosis.