Compacting, Picking and Growing for Unforgetting Continual Learning

Continual lifelong learning is essential to many applications. In this paper, we propose a simple but effective approach to continual deep learning. Our approach leverages the principles of deep model compression with weight pruning, critical weights selection, and progressive networks expansion. By enforcing their integration in an iterative manner, we introduce an incremental learning method that is scalable to the number of sequential tasks in a continual learning process. Our approach is easy to implement and owns several favorable characteristics. First, it can avoid forgetting (i.e., learn new tasks while remembering all previous tasks). Second, it allows model expansion but can maintain the model compactness when handling sequential tasks. Besides, through our compaction and selection/expanding mechanism, we show that the knowledge accumulated through learning previous tasks is helpful to adapt to a better model for the new tasks compared to training the models independently with tasks. Experimental results show that our approach can incrementally learn a deep model to tackle multiple tasks without forgetting, while the model compactness is maintained with the performance more satisfiable than ndividual task training.