  Catastrophic forgetting is a challenge issue in continual learning when a
deep neural network forgets the knowledge acquired from the former task after
learning on subsequent tasks. However, existing methods try to find the joint
distribution of parameters shared with all tasks. This idea can be questionable
because this joint distribution may not present when the number of tasks
increase. On the other hand, It also leads to "long-term" memory issue when the
network capacity is limited since adding tasks will "eat" the network capacity.
In this paper, we proposed a Soft Parameters Pruning (SPP) strategy to reach
the trade-off between short-term and long-term profit of a learning model by
freeing those parameters less contributing to remember former task domain
knowledge to learn future tasks, and preserving memories about previous tasks
via those parameters effectively encoding knowledge about tasks at the same
time. The SPP also measures the importance of parameters by information entropy
in a label free manner. The experiments on several tasks shows SPP model
achieved the best performance compared with others state-of-the-art methods.
Experiment results also indicate that our method is less sensitive to
hyper-parameter and better generalization. Our research suggests that a softer
strategy, i.e. approximate optimize or sub-optimal solution, will benefit
alleviating the dilemma of memory. The source codes are available at
https://github.com/lehaifeng/Learning_by_memory.

Machine Learning