  This work identifies a simple pre-training mechanism that leads to
representations exhibiting better continual and transfer learning. This
mechanism -- the repeated resetting of weights in the last layer, which we
nickname "zapping" -- was originally designed for a meta-continual-learning
procedure, yet we show it is surprisingly applicable in many settings beyond
both meta-learning and continual learning. In our experiments, we wish to
transfer a pre-trained image classifier to a new set of classes, in a few
shots. We show that our zapping procedure results in improved transfer accuracy
and/or more rapid adaptation in both standard fine-tuning and continual
learning settings, while being simple to implement and computationally
efficient. In many cases, we achieve performance on par with state of the art
meta-learning without needing the expensive higher-order gradients, by using a
combination of zapping and sequential learning. An intuitive explanation for
the effectiveness of this zapping procedure is that representations trained
with repeated zapping learn features that are capable of rapidly adapting to
newly initialized classifiers. Such an approach may be considered a
computationally cheaper type of, or alternative to, meta-learning rapidly
adaptable features with higher-order gradients. This adds to recent work on the
usefulness of resetting neural network parameters during training, and invites
further investigation of this mechanism.
