Deep Lifelong Learning Optimization Algorithm in Dense Region Fusion

Abstract

Deep lifelong learning models can learn new information continuously while minimizing the impact on previously acquired knowledge, and thus adapt to changing data. However, existing optimization approaches for deep lifelong learning cannot simultaneously satisfy the following conditions: unrestricted learning of new data, no use of old data, and no increase in model parameters. To address this problem, a deep lifelong learning optimization algorithm based on dense region fusion (DLLO-DRF) is proposed. This algorithm first obtains models for each stage of lifelong learning, and divides the model parameters for each stage into multiple regions based on the parameter values. Then, based on the dispersion of the parameter distribution, dense regions are dynamically obtained from the divided regions, and the parameters of the dense regions are averaged and fused to optimize the model. Finally, extensive experiments are conducted on the self-labeled transmission line defect dataset, and the results show that DLLO-DRF has the best performance among various comparative algorithms.