Neural networks often suffer from catastrophic interference (CI): performance on previously learned tasks drops off significantly when learning a new task. This contrasts strongly with humans, who can sequentially learn new tasks without appreciably forgetting previous tasks. Prior work has explored various techniques for mitigating CI such as regularization, rehearsal, generative replay, and distillation methods. The current work takes a different approach, one guided by cognitive science research showing that in naturalistic environments, the probability of encountering a task decreases as a power-law of the time since it was last performed. We argue that a realistic evaluation of techniques for the mitigation of CI should be performed in simulated naturalistic learning environments. Thus, we evaluate the extent of mitigation of CI when training simple rehearsal-based methods in power-law environments similar to the ones humans face. Our work explores this novel rehearsal-based approach for a domain-incremental task: learning permutations in the MNIST task. We compare our rehearsal environment with other baselines to show its efficacy in promoting continual learning. Additionally, we investigate whether this environment shows forward facilitation, i.e., faster learning of later tasks. Next, we explore the robustness of our learning environment to the number of tasks, model size, and amount of data rehearsed after each task. Notably, our results show that the performance is comparable or superior to that of models trained using popular regularization methods and also to rehearsals in non-power-law environments. The benefits of this training paradigm include simplicity and the lack of a need for extra neural circuitry. In addition, because our method is orthogonal to other methods, future research can combine training in power-law environments with other continual learning mechanisms.

翻译：神经网络常受灾难性干扰（Catastrophic Interference, CI）影响：学习新任务时，先前习得任务的性能显著下降。这与人类形成鲜明对比——人类能顺序学习新任务而极少遗忘旧任务。先前研究探索了多种缓解CI的技术，如正则化、重演、生成式回放和蒸馏方法。本研究另辟蹊径，以认知科学发现为指导：在自然环境中，任务被遇到的概率与自上次执行后的时间呈幂律递减关系。我们认为，应在模拟自然学习环境中对缓解CI的技术进行真实评估。因此，我们在人类面临的类幂律分布环境中，评估基于简单重演法的训练对CI的缓解程度。本研究针对领域递增任务（MNIST任务中的排列学习）探索了这种新颖的重演方法。我们将所提重演环境与其他基线对比，以证明其在促进持续学习方面的有效性。此外，我们检验该环境是否呈现前向促进效应（即后续任务学习速度加快）。接着探究学习环境对任务数量、模型规模和任务后重演数据量的鲁棒性。值得注意的是，结果表明：我们的性能与使用流行正则化方法训练的模型相当或更优，亦优于非幂律分布环境中的重演方法。该训练范式的优势包括简洁性及无需额外神经回路。由于本方法与其他方法正交，未来研究可将幂律环境训练与其它持续学习机制相结合。