  In recent years we have witnessed a renewed interest in machine learning
methodologies, especially for deep representation learning, that could overcome
basic i.i.d. assumptions and tackle non-stationary environments subject to
various distributional shifts or sample selection biases. Within this context,
several computational approaches based on architectural priors, regularizers
and replay policies have been proposed with different degrees of success
depending on the specific scenario in which they were developed and assessed.
However, designing comprehensive hybrid solutions that can flexibly and
generally be applied with tunable efficiency-effectiveness trade-offs still
seems a distant goal. In this paper, we propose "Architect, Regularize and
Replay" (ARR), an hybrid generalization of the renowned AR1 algorithm and its
variants, that can achieve state-of-the-art results in classic scenarios (e.g.
class-incremental learning) but also generalize to arbitrary data streams
generated from real-world datasets such as CIFAR-100, CORe50 and ImageNet-1000.
