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. Existing continual learning methods
alleviate this problem through regularisation, parameter isolation, or
rehearsal, and are typically evaluated on benchmarks consisting of a handful of
tasks. We propose a novel conceptual approach to continual classification that
aims to disentangle class-specific information that needs to be memorised from
the class-agnostic knowledge that encapsulates generalization. We store the
former in a buffer that can be easily pruned or updated when new categories
arrive, while the latter is represented with a neural network that generalizes
across tasks. We show that the class-agnostic network does not suffer from
catastrophic forgetting and by leveraging it to perform classification, we
improve accuracy on past tasks over time. In addition, our approach supports
open-set classification and one-shot generalization. To test our conceptual
framework, we introduce Infinite dSprites, a tool for creating continual
classification and disentanglement benchmarks of arbitrary length with full
control over generative factors. We show that over a sufficiently long time
horizon all major types of continual learning methods break down, while our
approach enables continual learning over hundreds of tasks with explicit
control over memorization and forgetting.

机器学习系统的持续学习能力受到灾难性遗忘的限制，本文提出一种新的概念性方法来解决持续分类中的问题，通过区分需要记忆的类别特定信息和总结泛化的类别无关知识，并且通过利用类别无关网络来执行分类，提高随时间推移过去任务的准确性，同时支持开放式分类和一次性泛化模型。