  Continual learning tackles the setting of learning different tasks
sequentially. Despite the lots of previous solutions, most of them still suffer
significant forgetting or expensive memory cost. In this work, targeted at
these problems, we first study the continual learning process through the lens
of information theory and observe that forgetting of a model stems from the
loss of \emph{information gain} on its parameters from the previous tasks when
learning a new task. From this viewpoint, we then propose a novel continual
learning approach called Bit-Level Information Preserving (BLIP) that preserves
the information gain on model parameters through updating the parameters at the
bit level, which can be conveniently implemented with parameter quantization.
More specifically, BLIP first trains a neural network with weight quantization
on the new incoming task and then estimates information gain on each parameter
provided by the task data to determine the bits to be frozen to prevent
forgetting. We conduct extensive experiments ranging from classification tasks
to reinforcement learning tasks, and the results show that our method produces
better or on par results comparing to previous state-of-the-arts. Indeed, BLIP
achieves close to zero forgetting while only requiring constant memory
overheads throughout continual learning.
