  While recent continual learning methods largely alleviate the catastrophic
problem on toy-sized datasets, some issues remain to be tackled to apply them
to real-world problem domains. First, a continual learning model should
effectively handle catastrophic forgetting and be efficient to train even with
a large number of tasks. Secondly, it needs to tackle the problem of
order-sensitivity, where the performance of the tasks largely varies based on
the order of the task arrival sequence, as it may cause serious problems where
fairness plays a critical role (e.g. medical diagnosis). To tackle these
practical challenges, we propose a novel continual learning method that is
scalable as well as order-robust, which instead of learning a completely shared
set of weights, represents the parameters for each task as a sum of task-shared
and sparse task-adaptive parameters. With our Additive Parameter Decomposition
(APD), the task-adaptive parameters for earlier tasks remain mostly unaffected,
where we update them only to reflect the changes made to the task-shared
parameters. This decomposition of parameters effectively prevents catastrophic
forgetting and order-sensitivity, while being computation- and
memory-efficient. Further, we can achieve even better scalability with APD
using hierarchical knowledge consolidation, which clusters the task-adaptive
parameters to obtain hierarchically shared parameters. We validate our network
with APD, APD-Net, on multiple benchmark datasets against state-of-the-art
continual learning methods, which it largely outperforms in accuracy,
scalability, and order-robustness.
