  Artificial neural networks face the well-known problem of catastrophic
forgetting. What's worse, the degradation of previously learned skills becomes
more severe as the task sequence increases, known as the long-term catastrophic
forgetting. It is due to two facts: first, as the model learns more tasks, the
intersection of the low-error parameter subspace satisfying for these tasks
becomes smaller or even does not exist; second, when the model learns a new
task, the cumulative error keeps increasing as the model tries to protect the
parameter configuration of previous tasks from interference. Inspired by the
memory consolidation mechanism in mammalian brains with synaptic plasticity, we
propose a confrontation mechanism in which Adversarial Neural Pruning and
synaptic Consolidation (ANPyC) is used to overcome the long-term catastrophic
forgetting issue. The neural pruning acts as long-term depression to prune
task-irrelevant parameters, while the novel synaptic consolidation acts as
long-term potentiation to strengthen task-relevant parameters. During the
training, this confrontation achieves a balance in that only crucial parameters
remain, and non-significant parameters are freed to learn subsequent tasks.
ANPyC avoids forgetting important information and makes the model efficient to
learn a large number of tasks. Specifically, the neural pruning iteratively
relaxes the current task's parameter conditions to expand the common parameter
subspace of the task; the synaptic consolidation strategy, which consists of a
structure-aware parameter-importance measurement and an element-wise parameter
updating strategy, decreases the cumulative error when learning new tasks. The
full source code is available at https://github.com/GeoX-Lab/ANPyC.
