  Continual learning has been a major problem in the deep learning community,
where the main challenge is how to effectively learn a series of newly arriving
tasks without forgetting the knowledge of previous tasks. Initiated by Learning
without Forgetting (LwF), many of the existing works report that knowledge
distillation is effective to preserve the previous knowledge, and hence they
commonly use a soft label for the old task, namely a knowledge distillation
(KD) loss, together with a class label for the new task, namely a cross entropy
(CE) loss, to form a composite loss for a single neural network. However, this
approach suffers from learning the knowledge by a CE loss as a KD loss often
more strongly influences the objective function when they are in a competitive
situation within a single network. This could be a critical problem
particularly in a class incremental scenario, where the knowledge across tasks
as well as within the new task, both of which can only be acquired by a CE
loss, is essentially learned due to the existence of a unified classifier. In
this paper, we propose a novel continual learning method, called
Split-and-Bridge, which can successfully address the above problem by partially
splitting a neural network into two partitions for training the new task
separated from the old task and re-connecting them for learning the knowledge
across tasks. In our thorough experimental analysis, our Split-and-Bridge
method outperforms the state-of-the-art competitors in KD-based continual
learning.
