  In continual learning, plasticity refers to the ability of an agent to
quickly adapt to new information. Neural networks are known to lose plasticity
when processing non-stationary data streams. In this paper, we propose L2 Init,
a simple approach for maintaining plasticity by incorporating in the loss
function L2 regularization toward initial parameters. This is very similar to
standard L2 regularization (L2), the only difference being that L2 regularizes
toward the origin. L2 Init is simple to implement and requires selecting only a
single hyper-parameter. The motivation for this method is the same as that of
methods that reset neurons or parameter values. Intuitively, when recent losses
are insensitive to particular parameters, these parameters should drift toward
their initial values. This prepares parameters to adapt quickly to new tasks.
On problems representative of different types of nonstationarity in continual
supervised learning, we demonstrate that L2 Init most consistently mitigates
plasticity loss compared to previously proposed approaches.
