Challenging Common Assumptions about Catastrophic Forgetting

• URL: http://arxiv.org/abs/2207.04543v2
• Date: Mon, 15 May 2023 22:27:14 GMT
• Title: Challenging Common Assumptions about Catastrophic Forgetting
• Authors: Timoth\'ee Lesort, Oleksiy Ostapenko, Diganta Misra, Md Rifat Arefin, Pau Rodr\'iguez, Laurent Charlin, Irina Rish
• Abstract summary: We study the progressive knowledge accumulation (KA) in DNNs trained with gradient-based algorithms in long sequences of tasks with data re-occurrence. We propose a new framework, SCoLe, to investigate KA and discover that catastrophic forgetting has a limited effect on DNNs trained with SGD.
• Score: 13.1202659074346
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Building learning agents that can progressively learn and accumulate knowledge is the core goal of the continual learning (CL) research field. Unfortunately, training a model on new data usually compromises the performance on past data. In the CL literature, this effect is referred to as catastrophic forgetting (CF). CF has been largely studied, and a plethora of methods have been proposed to address it on short sequences of non-overlapping tasks. In such setups, CF always leads to a quick and significant drop in performance in past tasks. Nevertheless, despite CF, recent work showed that SGD training on linear models accumulates knowledge in a CL regression setup. This phenomenon becomes especially visible when tasks reoccur. We might then wonder if DNNs trained with SGD or any standard gradient-based optimization accumulate knowledge in such a way. Such phenomena would have interesting consequences for applying DNNs to real continual scenarios. Indeed, standard gradient-based optimization methods are significantly less computationally expensive than existing CL algorithms. In this paper, we study the progressive knowledge accumulation (KA) in DNNs trained with gradient-based algorithms in long sequences of tasks with data re-occurrence. We propose a new framework, SCoLe (Scaling Continual Learning), to investigate KA and discover that catastrophic forgetting has a limited effect on DNNs trained with SGD. When trained on long sequences with data sparsely re-occurring, the overall accuracy improves, which might be counter-intuitive given the CF phenomenon. We empirically investigate KA in DNNs under various data occurrence frequencies and propose simple and scalable strategies to increase knowledge accumulation in DNNs.

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