  Most standard learning approaches lead to fragile models which are prone to
drift when sequentially trained on samples of a different nature - the
well-known "catastrophic forgetting" issue. In particular, when a model
consecutively learns from different visual domains, it tends to forget the past
domains in favor of the most recent ones. In this context, we show that one way
to learn models that are inherently more robust against forgetting is domain
randomization - for vision tasks, randomizing the current domain's distribution
with heavy image manipulations. Building on this result, we devise a
meta-learning strategy where a regularizer explicitly penalizes any loss
associated with transferring the model from the current domain to different
"auxiliary" meta-domains, while also easing adaptation to them. Such
meta-domains are also generated through randomized image manipulations. We
empirically demonstrate in a variety of experiments - spanning from
classification to semantic segmentation - that our approach results in models
that are less prone to catastrophic forgetting when transferred to new domains.
