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：神经网络常因灾难性干扰（CI）而受损：在学习新任务时，先前已学任务的表现显著下降。这与人类形成鲜明对比——人类能够依次学习新任务，且不会明显遗忘之前的知识。已有研究探索了多种缓解CI的技术，如正则化、复述、生成式回放和蒸馏方法。本研究另辟蹊径，受认知科学研究启发：在自然环境中，任务出现的概率随自上次执行时间的增加呈幂律衰减。我们认为，对缓解CI技术的评估应在模拟自然学习环境中进行，才能获得现实评价。因此，我们评估了在类似人类面对的幂律环境中，训练简单复述方法对CI的缓解程度。本研究针对域增量任务探索了这一新颖的复述方法：学习MNIST任务中的排列。我们将该复述环境与其他基准方法比较，证明其促进持续学习的有效性。此外，我们研究了该环境是否表现出前向促进作用，即后续任务的学习速度加快。接着，我们探究了学习环境对任务数量、模型规模及每项任务后复述数据量的稳健性。值得注意的是，我们的结果显示，该方法的性能与使用流行正则化方法训练的模型相当或更优，也优于非幂律环境中的复述方法。该训练范式的优势包括简单性以及无需额外神经回路。此外，由于我们的方法与其他方法正交，未来研究可将幂律环境训练与其他持续学习机制结合。