  In lifelong learning, we wish to maintain and update a model (e.g., a neural
network classifier) in the presence of new classification tasks that arrive
sequentially. In this paper, we propose a learn-prune-share (LPS) algorithm
which addresses the challenges of catastrophic forgetting, parsimony, and
knowledge reuse simultaneously. LPS splits the network into task-specific
partitions via an ADMM-based pruning strategy. This leads to no forgetting,
while maintaining parsimony. Moreover, LPS integrates a novel selective
knowledge sharing scheme into this ADMM optimization framework. This enables
adaptive knowledge sharing in an end-to-end fashion. Comprehensive experimental
results on two lifelong learning benchmark datasets and a challenging
real-world radio frequency fingerprinting dataset are provided to demonstrate
the effectiveness of our approach. Our experiments show that LPS consistently
outperforms multiple state-of-the-art competitors.
