Parameter-Level Soft-Masking for Continual Learning
Tatsuya Konishi Mori Kurokawa Chihiro Ono Zixuan Ke Gyuhak Kim Bing Liu
Existing research on task incremental learning in continual learning has
primarily focused on preventing catastrophic forgetting (CF). Although several
techniques have achieved learning with no CF, they attain it by letting each
task monopolize a sub-network in a shared network, which seriously limits
knowledge transfer (KT) and causes over-consumption of the network capacity,
i.e., as more tasks are learned, the performance deteriorates. The goal of this
paper is threefold: (1) overcoming CF, (2) encouraging KT, and (3) tackling the
capacity problem. A novel technique (called SPG) is proposed that soft-masks
(partially blocks) parameter updating in training based on the importance of
each parameter to old tasks. Each task still uses the full network, i.e., no
monopoly of any part of the network by any task, which enables maximum KT and
reduction in capacity usage. To our knowledge, this is the first work that
soft-masks a model at the parameter-level for continual learning. Extensive
experiments demonstrate the effectiveness of SPG in achieving all three
objectives. More notably, it attains significant transfer of knowledge not only
among similar tasks (with shared knowledge) but also among dissimilar tasks
(with little shared knowledge) while mitigating CF.