The ability of machine learning systems to learn continually is hindered by catastrophic forgetting, the tendency of neural networks to overwrite existing knowledge when learning a new task. Continual learning methods alleviate this problem through regularization, parameter isolation, or rehearsal, but they are typically evaluated on benchmarks comprising only a handful of tasks. In contrast, humans are able to learn continually in dynamic, open-world environments, effortlessly achieving one-shot memorization of unfamiliar objects and reliably recognizing them under various transformations. To make progress towards closing this gap, we introduce Infinite dSprites, a parsimonious tool for creating continual classification and disentanglement benchmarks of arbitrary length and with full control over generative factors. We show that over a sufficiently long time horizon, the performance of all major types of continual learning methods deteriorates on this simple benchmark. Thus, Infinite dSprites highlights an important aspect of continual learning that has not received enough attention so far: given a finite modelling capacity and an arbitrarily long learning horizon, efficient learning requires memorizing class-specific information and accumulating knowledge about general mechanisms. In a simple setting with direct supervision on the generative factors, we show how learning class-agnostic transformations offers a way to circumvent catastrophic forgetting and improve classification accuracy over time. Our approach sets the stage for continual learning over hundreds of tasks with explicit control over memorization and forgetting, emphasizing open-set classification and one-shot generalization.

翻译：机器学习系统持续学习的能力受到灾难性遗忘的阻碍——神经网络在学习新任务时倾向于覆盖已有知识。持续学习方法通过正则化、参数隔离或重演来缓解这一问题，但它们通常在仅包含少量任务的基准测试上进行评估。相比之下，人类能够在动态的开放世界环境中持续学习，轻松实现对陌生对象的一次性记忆，并在各种变换下可靠地识别它们。为弥合这一差距，我们引入了无限dSprites——一种简洁的工具，用于创建任意长度、且完全可控生成因子的持续分类与解耦基准测试。我们证明，在足够长的时间跨度下，所有主要类型的持续学习方法在此简单基准上的性能均会退化。因此，无限dSprites凸显了持续学习中一个迄今尚未得到足够重视的重要方面：给定有限建模能力和任意长的学习周期，高效学习需要记忆类别特定信息并积累关于通用机制的知识。在直接监督生成因子的简单设置中，我们展示了学习类别无关变换如何提供规避灾难性遗忘并随时间提高分类准确率的途径。我们的方法为在显式控制记忆与遗忘的条件下处理数百个任务的持续学习奠定了基础，强调了开放集分类与一次性泛化。