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

Architect, Regularize and Replay (ARR): a Flexible Hybrid Approach for Continual Learning