By default, neural networks learn on all training data at once. When such a
model is trained on sequential chunks of new data, it tends to catastrophically
forget how to handle old data. In this work we investigate how continual
learners learn and forget representations. We observe two phenomena: knowledge
accumulation, i.e. the improvement of a representation over time, and feature
forgetting, i.e. the loss of task-specific representations. To better
understand both phenomena, we introduce a new analysis technique called task
exclusion comparison. If a model has seen a task and it has not forgotten all
the task-specific features, then its representation for that task should be
better than that of a model that was trained on similar tasks, but not that
exact one. Our image classification experiments show that most task-specific
features are quickly forgotten, in contrast to what has been suggested in the
past. Further, we demonstrate how some continual learning methods, like replay,
and ideas from representation learning affect a continually learned
representation. We conclude by observing that representation quality is tightly
correlated with continual learning performance.

本文研究神经网络在不断学习时如何学习和忘记其代表特征并引入了任务排除比较等新的分析技术。研究发现大部分特定任务的特征很快被遗忘，但回放和代表特征学习等方法可影响代表特征及其品质与学习绩效的紧密相关性。