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.

Vincenzo Lomonaco, L.P. (2023). Architect, Regularize and Replay (ARR): a Flexible Hybrid Approach for Continual learning. Singapore : World Scientific Publishing [10.1142/13689].

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

Vincenzo Lomonaco;Lorenzo Pellegrini;Gabriele Graffieti;Davide Maltoni
2023

Abstract

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.
2023
Towards Human Brain Inspired Lifelong Learning
0
14
Vincenzo Lomonaco, L.P. (2023). Architect, Regularize and Replay (ARR): a Flexible Hybrid Approach for Continual learning. Singapore : World Scientific Publishing [10.1142/13689].
Vincenzo Lomonaco, Lorenzo Pellegrini, Gabriele Graffieti, Davide Maltoni
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11585/962793
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